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Risk Management in Trading by Davis Edwards

Abraham Maslow, asset allocation, asset-backed security, backtesting, Bear Stearns, Black-Scholes formula, Brownian motion, business cycle, computerized trading, correlation coefficient, Credit Default Swap, discrete time, diversified portfolio, financial engineering, fixed income, Glass-Steagall Act, global macro, implied volatility, intangible asset, interest rate swap, iterative process, John Meriwether, junk bonds, London Whale, Long Term Capital Management, low interest rates, margin call, Myron Scholes, Nick Leeson, p-value, paper trading, pattern recognition, proprietary trading, random walk, risk free rate, risk tolerance, risk/return, selection bias, shareholder value, Sharpe ratio, short selling, statistical arbitrage, statistical model, stochastic process, systematic trading, time value of money, transaction costs, value at risk, Wiener process, zero-coupon bond

Rare moves are not well described by typical behavior because they have different root causes than normal price moves. WHAT IS VALUE-AT-RISK? Value‐at‐risk uses a factor common to all financial instruments (daily changes in value caused by mark‐to‐market accounting) to establish an apples‐to‐ apples comparison of size across a wide variety of instruments. It is typically pronounced “var” and rhymes with “car.” In some cases, it is abbreviated VaR or V@R to distinguish it from the mathematical abbreviation for variance, which is commonly abbreviated var. 144 RISK MANAGEMENT IN TRADING KEY CONCEPT: VALUE AT RISK (VAR) IS DEFINED MATHEMATICALLY Value‐at‐risk is typically defined as the maximum expected loss on a financial instrument, or a portfolio of financial instruments, over a given period of time and a given level of confidence.

Extreme events are difficult to predict using VAR for several reasons. First, VAR is based on a common denominatorr that crosses commodity, Value-at-Risk 147 KEY CONCEPT: VALUE-AT-RISK IS A MEASURE OF SIZE Value‐at‐risk is way to describe the size of a trading position. It does this by describing how the value of trading positions is likely to change over time. As a measure of size, VAR doesn’t give much insight into the specific risks facing a trading position. ■ ■ ■ ■ ■ Value‐at‐risk is a measure of size. VAR is commonly used to set position limits. As a measure of size, VAR does not describe whether a transaction is likely to be a good or bad investment.

See unexpected loss unexpected loss, 240–241 V validation, data, 96–97 value of options, 204–207 value-at-risk limits, in practice, 170 value-at-risk sensitivity, 162–163 value-at-risk as size measure, 147 defined, 143–147 misuse of, 171–173 non-parametric, 167–169 parametric, 150–161 zero and, 164 VAR. See value-at-risk variables, 62–63 variance, 69–70 variance/covariance matrix, 165–167 vega, 203, 230–232 time and, 232 volatile earnings, created via hedging, 187 volatility, 69–70, 201 estimating, 153–161 volume notation, 202 W wrong-way risk, 255 Z zero coupon bonds, 49 zero, value-at-risk and, 164


pages: 416 words: 39,022

Asset and Risk Management: Risk Oriented Finance by Louis Esch, Robert Kieffer, Thierry Lopez

asset allocation, Brownian motion, business continuity plan, business process, capital asset pricing model, computer age, corporate governance, discrete time, diversified portfolio, fixed income, implied volatility, index fund, interest rate derivative, iterative process, P = NP, p-value, random walk, risk free rate, risk/return, shareholder value, statistical model, stochastic process, transaction costs, value at risk, Wiener process, yield curve, zero-coupon bond

xix xix xxi PART I THE MASSIVE CHANGES IN THE WORLD OF FINANCE Introduction 1 The Regulatory Context 1.1 Precautionary surveillance 1.2 The Basle Committee 1.2.1 General information 1.2.2 Basle II and the philosophy of operational risk 1.3 Accounting standards 1.3.1 Standard-setting organisations 1.3.2 The IASB 2 Changes in Financial Risk Management 2.1 Definitions 2.1.1 Typology of risks 2.1.2 Risk management methodology 2.2 Changes in financial risk management 2.2.1 Towards an integrated risk management 2.2.2 The ‘cost’ of risk management 2.3 A new risk-return world 2.3.1 Towards a minimisation of risk for an anticipated return 2.3.2 Theoretical formalisation 1 2 3 3 3 3 5 9 9 9 11 11 11 19 21 21 25 26 26 26 vi Contents PART II EVALUATING FINANCIAL ASSETS Introduction 3 4 29 30 Equities 3.1 The basics 3.1.1 Return and risk 3.1.2 Market efficiency 3.1.3 Equity valuation models 3.2 Portfolio diversification and management 3.2.1 Principles of diversification 3.2.2 Diversification and portfolio size 3.2.3 Markowitz model and critical line algorithm 3.2.4 Sharpe’s simple index model 3.2.5 Model with risk-free security 3.2.6 The Elton, Gruber and Padberg method of portfolio management 3.2.7 Utility theory and optimal portfolio selection 3.2.8 The market model 3.3 Model of financial asset equilibrium and applications 3.3.1 Capital asset pricing model 3.3.2 Arbitrage pricing theory 3.3.3 Performance evaluation 3.3.4 Equity portfolio management strategies 3.4 Equity dynamic models 3.4.1 Deterministic models 3.4.2 Stochastic models 35 35 35 44 48 51 51 55 56 69 75 79 85 91 93 93 97 99 103 108 108 109 Bonds 4.1 Characteristics and valuation 4.1.1 Definitions 4.1.2 Return on bonds 4.1.3 Valuing a bond 4.2 Bonds and financial risk 4.2.1 Sources of risk 4.2.2 Duration 4.2.3 Convexity 4.3 Deterministic structure of interest rates 4.3.1 Yield curves 4.3.2 Static interest rate structure 4.3.3 Dynamic interest rate structure 4.3.4 Deterministic model and stochastic model 4.4 Bond portfolio management strategies 4.4.1 Passive strategy: immunisation 4.4.2 Active strategy 4.5 Stochastic bond dynamic models 4.5.1 Arbitrage models with one state variable 4.5.2 The Vasicek model 115 115 115 116 119 119 119 121 127 129 129 130 132 134 135 135 137 138 139 142 Contents 4.5.3 The Cox, Ingersoll and Ross model 4.5.4 Stochastic duration 5 Options 5.1 Definitions 5.1.1 Characteristics 5.1.2 Use 5.2 Value of an option 5.2.1 Intrinsic value and time value 5.2.2 Volatility 5.2.3 Sensitivity parameters 5.2.4 General properties 5.3 Valuation models 5.3.1 Binomial model for equity options 5.3.2 Black and Scholes model for equity options 5.3.3 Other models of valuation 5.4 Strategies on options 5.4.1 Simple strategies 5.4.2 More complex strategies PART III GENERAL THEORY OF VaR Introduction vii 145 147 149 149 149 150 153 153 154 155 157 160 162 168 174 175 175 175 179 180 6 Theory of VaR 6.1 The concept of ‘risk per share’ 6.1.1 Standard measurement of risk linked to financial products 6.1.2 Problems with these approaches to risk 6.1.3 Generalising the concept of ‘risk’ 6.2 VaR for a single asset 6.2.1 Value at Risk 6.2.2 Case of a normal distribution 6.3 VaR for a portfolio 6.3.1 General results 6.3.2 Components of the VaR of a portfolio 6.3.3 Incremental VaR 181 181 181 181 184 185 185 188 190 190 193 195 7 VaR Estimation Techniques 7.1 General questions in estimating VaR 7.1.1 The problem of estimation 7.1.2 Typology of estimation methods 7.2 Estimated variance–covariance matrix method 7.2.1 Identifying cash flows in financial assets 7.2.2 Mapping cashflows with standard maturity dates 7.2.3 Calculating VaR 7.3 Monte Carlo simulation 7.3.1 The Monte Carlo method and probability theory 7.3.2 Estimation method 199 199 199 200 202 203 205 209 216 216 218 viii Contents 7.4 Historical simulation 7.4.1 Basic methodology 7.4.2 The contribution of extreme value theory 7.5 Advantages and drawbacks 7.5.1 The theoretical viewpoint 7.5.2 The practical viewpoint 7.5.3 Synthesis 8 Setting Up a VaR Methodology 8.1 Putting together the database 8.1.1 Which data should be chosen?

We therefore have the following definitive definition: pt = pt − p0 . The ‘value at risk’ of the asset in question for the duration t and the probability level q is defined as an amount termed VaR, so that the variation pt observed for the asset during the interval [0; t] will only be less than VaR with a probability of (1 − q): Pr[pt ≤ VaR] = 1 − q Or similarly: Pr[pt > VaR] = q By expressing as Fp and fp respectively the distribution function and density function of the random variable pt , we arrive at the definition of VaR in Figures 6.4 and 6.5. F∆p(x) 1 1–q VaR x Figure 6.4 Definition of VaR based on distribution function 5 In this chapter, the theory is presented on the basis of the value, the price of assets, portfolios etc.

This group of risks cannot of course be estimated using techniques designed for market risk. The techniques shown in this Part III do not therefore deal with these ‘event-related’ risks. This should not, however, prevent the wise risk manager from analysing his positions using value at risk theory, or from using ‘catastrophe scenarios’, in an effort to understand this type of exceptional risk. Theory of VaR 185 6.2 VaR FOR A SINGLE ASSET 6.2.1 Value at Risk In view of what has been set out in the previous paragraph, an index that allows estimation of the market risks facing an investor should: • • • • be independent of any distributional hypothesis; concern only downside risk, namely the risk of loss; measure the loss in question in a certain way; be valid for all types of assets and therefore either involve the various valuation models or be independent of these models.


Analysis of Financial Time Series by Ruey S. Tsay

Asian financial crisis, asset allocation, backpropagation, Bayesian statistics, Black-Scholes formula, Brownian motion, business cycle, capital asset pricing model, compound rate of return, correlation coefficient, data acquisition, discrete time, financial engineering, frictionless, frictionless market, implied volatility, index arbitrage, inverted yield curve, Long Term Capital Management, market microstructure, martingale, p-value, pattern recognition, random walk, risk free rate, risk tolerance, short selling, statistical model, stochastic process, stochastic volatility, telemarketer, transaction costs, value at risk, volatility smile, Wiener process, yield curve

Figure 7.1 shows the time plot of daily log returns of IBM stock from July 3, 1962 to December 31, 1998 for 9190 observations. 7.1 VALUE AT RISK There are several types of risk in financial markets. Credit risk, liquidity risk, and market risk are three examples. Value at risk (VaR) is mainly concerned with market risk. It is a single estimate of the amount by which an institution’s position in a risk category could decline due to general market movements during a given holding 256 257 -0.2 log return -0.1 0.0 0.1 VALUE AT RISK 1970 1980 year 1990 2000 Figure 7.1. Time plot of daily log returns of IBM stock from July 3, 1962 to December 31, 1998.

Assume that the tail probability is 0.05. The VaR quantile shown in Eq. (7.34) gives VaR = 3.03756%. Consequently, for a long position of $10 million, we have VaR = $10,000,000 × 0.0303756 = $303,756. If the tail probability is 0.01, the VaR is $497,425. The 5% VaR is slightly larger than that of Example 7.3, which uses a Gaussian AR(2)-GARCH(1, 1) model. The 1% VaR is larger than that of Case I of Example 7.3. Again, as expected, the effect of extreme values (i.e., heavy tails) on VaR is more pronounced when the tail probability used is small. 296 VALUE AT RISK An advantage of using explanatory variables is that the parameters are adaptive to the change in market conditions.

Denote the cumulative distribution function (CDF) of V () by F (x). We define the VaR of a long position over the time horizon with probability p as p = Pr[V () ≤ VaR] = F (VaR). (7.1) 258 VALUE AT RISK Since the holder of a long financial position suffers a loss when V () < 0, the VaR defined in Eq. (7.1) typically assumes a negative value when p is small. The negative sign signifies a loss. From the definition, the probability that the holder would encounter a loss greater than or equal to VaR over the time horizon is p. Alternatively, VaR can be interpreted as follows. With probability (1 − p), the potential loss encountered by the holder of the financial position over the time horizon is less than or equal to VaR.


pages: 483 words: 141,836

Red-Blooded Risk: The Secret History of Wall Street by Aaron Brown, Eric Kim

Abraham Wald, activist fund / activist shareholder / activist investor, Albert Einstein, algorithmic trading, Asian financial crisis, Atul Gawande, backtesting, Basel III, Bayesian statistics, Bear Stearns, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Black Swan, book value, business cycle, capital asset pricing model, carbon tax, central bank independence, Checklist Manifesto, corporate governance, creative destruction, credit crunch, Credit Default Swap, currency risk, disintermediation, distributed generation, diversification, diversified portfolio, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, experimental subject, fail fast, fear index, financial engineering, financial innovation, global macro, illegal immigration, implied volatility, independent contractor, index fund, John Bogle, junk bonds, Long Term Capital Management, loss aversion, low interest rates, managed futures, margin call, market clearing, market fundamentalism, market microstructure, Money creation, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, natural language processing, open economy, Pierre-Simon Laplace, power law, pre–internet, proprietary trading, quantitative trading / quantitative finance, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, shareholder value, Sharpe ratio, special drawing rights, statistical arbitrage, stochastic volatility, stock buybacks, stocks for the long run, tail risk, The Myth of the Rational Market, Thomas Bayes, too big to fail, transaction costs, value at risk, yield curve

To make this precise, I’m going to jump ahead and steal a concept that wasn’t fully fleshed out until 1992, value at risk (VaR). VaR is defined operationally. That means we specify a property VaR is supposed to have, and then try to figure out what number satisfies the property. For a 1 percent one-day VaR, the property is that one day in 100—1 percent of the time—a portfolio will lose more than the VaR amount over one day, assuming normal markets and no position changes in the portfolio. VaR can also be defined at different probability levels and over different time horizons. The 99 days in 100 in which markets are normal and you make money or lose less than the VaR amount are used as data for mathematical optimization.

For financial trading applications, the standard process is: Estimate a 95 percent one-day value at risk each day before trading begins. You estimate every trading day, even if systems are down or data are missing. Compare actual daily profit and loss (P&L) against the VaR prediction when the daily P&L becomes available. Test for the correct number of VaR breaks, within statistical error. Test that the breaks are independent in time and independent of the level of VaR. Once you have a reliable VaR system, collect data within the VaR limit. Investigate days when you lose more than the VaR amount, but supplement the observations with hypothetical scenarios and days in the past when your current positions would have suffered large losses.

However, the Securities and Exchange Commission in 1998 ordered financial firms to include risk information in their annual and quarterly reports, and suggested disclosing value at risk as one of the allowed methods. But I’ve never met an equity analyst, credit analyst, or investor who uses the VaR data reported in financial statement notes. The first thing a middle-office risk manager does is issue daily VaRs for every business unit and subunit. These predictions are tested against the actual profit or loss on the positions at the time of VaR computations. It’s usually two or three days before you have final profit and loss numbers for comparison. The inputs for the VaR computations come from the front office. If the inputs are delayed, you fill in guesses.


pages: 447 words: 104,258

Mathematics of the Financial Markets: Financial Instruments and Derivatives Modelling, Valuation and Risk Issues by Alain Ruttiens

algorithmic trading, asset allocation, asset-backed security, backtesting, banking crisis, Black Swan, Black-Scholes formula, Bob Litterman, book value, Brownian motion, capital asset pricing model, collateralized debt obligation, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, delta neutral, discounted cash flows, discrete time, diversification, financial engineering, fixed income, implied volatility, interest rate derivative, interest rate swap, low interest rates, managed futures, margin call, market microstructure, martingale, p-value, passive investing, proprietary trading, quantitative trading / quantitative finance, random walk, risk free rate, risk/return, Satyajit Das, seminal paper, Sharpe ratio, short selling, statistical model, stochastic process, stochastic volatility, time value of money, transaction costs, value at risk, volatility smile, Wiener process, yield curve, zero-coupon bond

short-term rates discount basis trading futures rate basis trading spot instruments skewness smiles, volatility smirks, volatility SML see security market line Sortino ratio sovereign bonds Spearman’s rank correlation coefficient special purpose vehicles (SPVs) specific risk speed sensitivity splines spot instruments bonds correlation modeling currencies forex swaps Gaussian hypothesis alternatives prices rates short-term rates volatility spreads SPVs see special purpose vehicles standardized futures contracts standard Wiener process see also dZ; general Wiener process stationarity stationary Markovian processes stochastic processes basis of Brownian motion definition of process diffusion processes discrete/continuous variables general Wiener process Markovian processes martingales probability reminders risk neutral probability standard Wiener process stationary/non-stationary processes terminology stock indexes basket options futures stock portfolios stock prices stock valuation book value method DCF method Gordon–Shapiro method real option method stocks without dividends stress tests Structural model Student distribution swaps bond duration conditional CRSs curves forwards ISDA second-generation swap points swap rate markets variance volatility see also forex swaps; interest rate swaps swaptions systematic factors Taiwan dollars (TWD) Taleb, Nassim Taylor series TE see Tracking Error term structure theoretical price forward foreign exchange futures theta time, continuous/discrete time horizon, VaR time value of option time-weighted rate of return (TWRR) Tiscali telecommunications Total total period, FRAs Toy see Black, Derman, Toy process Tracking Error (TE) tranches transfer functions Treasury bonds Treynor ratio trinomial trees TWD see Taiwan dollars TWRR see time-weighted rate of return Uhlenbeck see Ohrstein–Uhlenbeck unexpected credit loss United States dollars (USD) CRS swaps forward foreign exchange futures NDOs swap rates market volatility unwinding swaps USD see United States dollars valuation callable bonds credit derivatives IRSs stocks troubles value-at-risk (VaR) backtesting correlation troubles example important remarks methods parameters variants value-weighted indexes vanilla IRSs vanilla options vanilla swaps CRSs in-arrear swaps IRSs vanna VaR see value-at-risk variance-covariance method, VaR “variance gamma” process variance swaps Vasicek model VDAX index vega VIX index volatility annualized basket options correlation modeling curves delta-gamma neutral management derivatives dVega/dTime general Wiener process historical implied intraday volatility modeling option pricing practical issues realized models smiles smirks variance swaps vega volga vomma VXN index weather White see Hull and White model white noise AR process see also Brownian motion; standard Wiener process Wiener see general Wiener process; standard Wiener process WTI Crude Oil futures Yang–Zang volatility yield, convenience yield curves capital markets components CRS pricing cubic splines method definition EONIA/OIS swaps implied volatility interest rate options interpolations linear method methodology money markets points determination example polynomial curve methods swap curve swaps see also term structure yield to maturity (YTM) Z see dZ Zang see Yang–Zang volatility zero-coupon bonds zero-coupon rates see also spot instruments, rates zero-coupon swaps Z-score

In particular, if L = 0, that is, if it separates positive from negative returns, the Omega ratio corresponds to what has been introduced as the “Bernardo–Ledoit gain-loss ratio”.6 14.2 VaR OR VALUE-AT-RISK This section is mainly relative to a risk management tool with respect to market risk.7 The last sub-section concerns the case of the credit risk. The VaR is a risk measure that can be defined as the estimated possible loss, expressed as an amount of $(or any other currency), that can suffer a position or group of positions in financial market instruments, over a given horizon of time, with respect to some given probability level, called confidence level. The VaR measure thus rests on an assessment about the probability distributions of the prices of the instruments that compose the related risky position.

Contents Cover Series Title Page Copyright Dedication Foreword Main Notations Introduction Part I: The Deterministic Environment Chapter 1: Prior to the yield curve: spot and forward rates 1.1 INTEREST RATES, PRESENT AND FUTURE VALUES, INTEREST COMPOUNDING 1.2 DISCOUNT FACTORS 1.3 CONTINUOUS COMPOUNDING AND CONTINUOUS RATES 1.4 FORWARD RATES 1.5 THE NO ARBITRAGE CONDITION FURTHER READING Chapter 2: The term structure or yield curve 2.1 INTRODUCTION TO THE YIELD CURVE 2.2 THE YIELD CURVE COMPONENTS 2.3 BUILDING A YIELD CURVE: METHODOLOGY 2.4 AN EXAMPLE OF YIELD CURVE POINTS DETERMINATION 2.5 INTERPOLATIONS ON A YIELD CURVE FURTHER READING Chapter 3: Spot instruments 3.1 SHORT-TERM RATES 3.2 BONDS 3.3 CURRENCIES FURTHER READING Chapter 4: Equities and stock indexes 4.1 STOCKS VALUATION 4.2 STOCK INDEXES 4.3 THE PORTFOLIO THEORY FURTHER READING Chapter 5: Forward instruments 5.1 THE FORWARD FOREIGN EXCHANGE 5.2 FRAs 5.3 OTHER FORWARD CONTRACTS 5.4 CONTRACTS FOR DIFFERENCE (CFD) FURTHER READING Chapter 6: Swaps 6.1 DEFINITIONS AND FIRST EXAMPLES 6.2 PRIOR TO AN IRS SWAP PRICING METHOD 6.3 PRICING OF AN IRS SWAP 6.4 (RE)VALUATION OF AN IRS SWAP 6.5 THE SWAP (RATES) MARKET 6.6 PRICING OF A CRS SWAP 6.7 PRICING OF SECOND-GENERATION SWAPS FURTHER READING Chapter 7: Futures 7.1 INTRODUCTION TO FUTURES 7.2 FUTURES PRICING 7.3 FUTURES ON EQUITIES AND STOCK INDEXES 7.4 FUTURES ON SHORT-TERM INTEREST RATES 7.5 FUTURES ON BONDS 7.6 FUTURES ON CURRENCIES 7.7 FUTURES ON (NON-FINANCIAL) COMMODITIES FURTHER READING Part II: The Probabilistic Environment Chapter 8: The basis of stochastic calculus 8.1 STOCHASTIC PROCESSES 8.2 THE STANDARD WIENER PROCESS, OR BROWNIAN MOTION 8.3 THE GENERAL WIENER PROCESS 8.4 THE ITÔ PROCESS 8.5 APPLICATION OF THE GENERAL WIENER PROCESS 8.6 THE ITÔ LEMMA 8.7 APPLICATION OF THE ITô LEMMA 8.8 NOTION OF RISK NEUTRAL PROBABILITY 8.9 NOTION OF MARTINGALE ANNEX 8.1: PROOFS OF THE PROPERTIES OF dZ(t) ANNEX 8.2: PROOF OF THE ITÔ LEMMA FURTHER READING Chapter 9: Other financial models: from ARMA to the GARCH family 9.1 THE AUTOREGRESSIVE (AR) PROCESS 9.2 THE MOVING AVERAGE (MA) PROCESS 9.3 THE AUTOREGRESSION MOVING AVERAGE (ARMA) PROCESS 9.4 THE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) PROCESS 9.5 THE ARCH PROCESS 9.6 THE GARCH PROCESS 9.7 VARIANTS OF (G)ARCH PROCESSES 9.8 THE MIDAS PROCESS FURTHER READING Chapter 10: Option pricing in general 10.1 INTRODUCTION TO OPTION PRICING 10.2 THE BLACK–SCHOLES FORMULA 10.3 FINITE DIFFERENCE METHODS: THE COX–ROSS–RUBINSTEIN (CRR) OPTION PRICING MODEL 10.4 MONTE CARLO SIMULATIONS 10.5 OPTION PRICING SENSITIVITIES FURTHER READING Chapter 11: Options on specific underlyings and exotic options 11.1 CURRENCY OPTIONS 11.2 OPTIONS ON BONDS 11.3 OPTIONS ON INTEREST RATES 11.4 EXCHANGE OPTIONS 11.5 BASKET OPTIONS 11.6 BERMUDAN OPTIONS 11.7 OPTIONS ON NON-FINANCIAL UNDERLYINGS 11.8 SECOND-GENERATION OPTIONS, OR EXOTICS FURTHER READING Chapter 12: Volatility and volatility derivatives 12.1 PRACTICAL ISSUES ABOUT THE VOLATILITY 12.2 MODELING THE VOLATILITY 12.3 REALIZED VOLATILITY MODELS 12.4 MODELING THE CORRELATION 12.5 VOLATILITY AND VARIANCE SWAPS FURTHER READING Chapter 13: Credit derivatives 13.1 INTRODUCTION TO CREDIT DERIVATIVES 13.2 VALUATION OF CREDIT DERIVATIVES 13.3 CONCLUSION FURTHER READING Chapter 14: Market performance and risk measures 14.1 RETURN AND RISK MEASURES 14.2 VaR OR VALUE-AT-RISK FURTHER READING Chapter 15: Beyond the Gaussian hypothesis: potential troubles with derivatives valuation 15.1 ALTERNATIVES TO THE GAUSSIAN HYPOTHESIS 15.2 POTENTIAL TROUBLES WITH DERIVATIVES VALUATION FURTHER READING Bibliography Index For other titles in the Wiley Finance series please see www.wiley.com/finance This edition first published 2013 Copyright © 2013 Alain Ruttiens Registered office John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com.


Trading Risk: Enhanced Profitability Through Risk Control by Kenneth L. Grant

backtesting, business cycle, buy and hold, commodity trading advisor, correlation coefficient, correlation does not imply causation, delta neutral, diversification, diversified portfolio, financial engineering, fixed income, frictionless, frictionless market, George Santayana, global macro, implied volatility, interest rate swap, invisible hand, Isaac Newton, John Meriwether, Long Term Capital Management, managed futures, market design, Myron Scholes, performance metric, price mechanism, price stability, proprietary trading, risk free rate, risk tolerance, risk-adjusted returns, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, the scientific method, The Wealth of Nations by Adam Smith, transaction costs, two-sided market, uptick rule, value at risk, volatility arbitrage, yield curve, zero-coupon bond

ISBN 0-471-65091-9 Printed in the United States of America. 10 9 8 7 6 5 4 3 2 1 Contents PREFACE ix ACKNOWLEDGMENTS CHAPTER 1 The Risk Management Investment CHAPTER 2 Setting Performance Objectives Optimal Target Return Nominal Target Return Stop-Out Level The Beach CHAPTER 3 Understanding the Profit/Loss Patterns over Time And Now to Statistics, but First a Word (or More) about Time Series Construction Time Units Time Spans Graphical Representation of Daily P/L Histogram of P/L Observations Statistics A Tribute to Sir Isaac Newton Average P/L Standard Deviation Sharpe Ratio Median P/L Percentage of Winning Days Performance Ratio, Average P/L, Winning Days versus Losing Days xiv 1 19 21 24 26 32 37 39 40 43 48 51 53 53 56 57 65 68 68 69 v vi CONTENTS Drawdown Correlations 70 73 Putting It All Together CHAPTER 4 The Risk Components of an Individual Portfolio Historical Volatility Options Implied Volatility Correlation Value at Risk (VaR) Justification for VaR Calculations Types of VaR Calculations Testing VaR Accuracy Setting VaR Parameters Use of VaR Calculation in Portfolio Management Scenario Analysis Technical Analysis CHAPTER 5 Setting Appropriate Exposure Levels (Rule 1) Determining the Appropriate Ranges of Exposure Method 1: Inverted Sharpe Ratio Method 2: Managing Volatility as a Percentage of Trading Capital Drawdowns and Netting Risk Asymmetric Payoff Function CHAPTER 6 Adjusting Portfolio Exposure (Rule 2) Size of Individual Positions Directional Bias Position Level Volatility Time Horizon Diversification Leverage Optionality Nonlinear Pricing Dynamics Relationship between Strike Price and Underlying Price (Moneyness) 79 81 84 86 90 91 92 94 98 99 102 104 106 109 110 111 114 129 130 133 134 135 141 142 144 146 148 149 149 vii Contents Implied Volatility Asymmetric Payoff Functions Leverage Characteristics Summary CHAPTER 7 150 150 151 154 The Risk Components of an Individual Trade Your Transaction Performance Key Components of a Transactions-Level Database Defining a Transaction Position Snapshot Statistics Core Transactions-Level Statistics Trade Level P/L Holding Period Average P/L P/L per Dollar Invested (Weighted Average P/L) Average Holding Period P/L by Security (P/L Attribution) Long Side P/L versus Short Side P/L Correlation Analysis Number of Daily Transactions Capital Invested Net Market Value (Raw) Net Market Value (Absolute Value) Number of Positions Holding Periods Volatility/VaR Other Correlations Final Word on Correlation Performance Success Metrics Methods for Improving Performance Ratios Performance Ratio Components Maximizing Your P/L Profitability Concentration (90/10) Ratio 155 156 157 158 160 161 162 162 163 164 164 165 166 168 170 171 172 173 174 175 177 179 179 184 189 190 192 200 Putting It All Together 208 CHAPTER 8 213 Bringin’ It on Home Make a Plan and Stick to It If the Plan’s Not Working, Change the Plan Seek to Trade with an “Edge” 214 218 219 viii CONTENTS Structural Inefficiencies Methodological Inefficiencies 220 223 Play Your P/L Avoid Surprises—Especially to Yourself Seek to Maximize Your Performance at the Margin Seek Nonmonetary Benefits Apply Liberal Doses of Humility and Humor Be Healthy/Cultivate Other Interests 236 237 242 244 APPENDIX 245 Optimal f and Risk of Ruin 226 234 Optimal f Risk of Ruin 246 250 INDEX 253 Contents Preface Make voyages.

As is the case elsewhere, this limitation contains a hidden opportunity: I believe it is useful to compare correlations between securities across different time spans so as to gain a better The Risk Components of an Individual Portfolio 91 understanding of interactive pricing dynamics across the fullest range of available market conditions. VALUE AT RISK (VaR) Through the efforts of modern-day financial engineers, a new paradigm has emerged: It is now possible, nay, even fashionable, to combine the concepts of volatility and correlation into a single, portfolio-based exposure estimate. This work, most of which has been conducted over the past 15 or so years, is most broadly synthesized under the heading of Value at Risk, which is now thought of as the standard methodology for risk management in the financial services industry.

Perhaps the best alternative at your disposal in this regard is the results of a Value at Risk (VaR) calculation, which have the advantage of being based on current portfolio characteristics. If you have access to a VaR calculation, it is therefore possible to substitute this figure into the denominator of the Sharpe Ratio, as long as you take care to scale down the confidence interval statistic to the one standard deviation level (for example, if you are using a 95th percentile VaR, you can map it back into a one standard deviation figure by dividing by 1.96). If you choose not to use a VaR approach, the best alternative is simply to calculate a one standard deviation P/L volatility.


Commodity Trading Advisors: Risk, Performance Analysis, and Selection by Greg N. Gregoriou, Vassilios Karavas, François-Serge Lhabitant, Fabrice Douglas Rouah

Asian financial crisis, asset allocation, backtesting, buy and hold, capital asset pricing model, collateralized debt obligation, commodity trading advisor, compound rate of return, constrained optimization, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, discrete time, distributed generation, diversification, diversified portfolio, dividend-yielding stocks, financial engineering, fixed income, global macro, high net worth, implied volatility, index arbitrage, index fund, interest rate swap, iterative process, linear programming, London Interbank Offered Rate, Long Term Capital Management, managed futures, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, p-value, Pareto efficiency, Performance of Mutual Funds in the Period, Ponzi scheme, proprietary trading, quantitative trading / quantitative finance, random walk, risk free rate, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, stochastic process, survivorship bias, systematic trading, tail risk, technology bubble, transaction costs, value at risk, zero-sum game

Thus, investors needed a more precise measure of downside risk. With the value at risk (VaR) approach, it is possible to measure the amount of portfolio wealth that can be lost over a given period of time with a certain probability. VaR has become a widely used risk management tool. The Basel Accord of 1988, for example, requires commercial banks to compute VaR in setting their minimum capital requirements (see Jorion 2001). One of the main advantages of VaR is that it works across different asset classes such as stocks and bonds. Further, VaR often is used as an ex-post measure to evaluate the current exposure to market risk and determine whether this exposure should be reduced.

Our next step is to provide some Value at Risk analysis. 194 RISK AND MANAGED FUTURES INVESTING Diversified Excess Returns 15.00% 10.00% 5.00% 0.00% –5.00% –10.00% –20.00% –15.00% –10.00% –5.00% 0.00% 5.00% 10.00% 15.00% S&P 100 Excess Returns Diversified Trading Mimicking Portfolio Systematic Excess Returns FIGURE 9.6 Mimicking Portfolio Returns for the Barclay Diversified Trading Index 0.200 0.150 0.100 0.050 0.000 –0.050 –0.100 –0.175 –0.150 –0.125 –0.100 –0.075 –0.050 –0.025 0.000 0.025 0.050 0.075 0.100 0.125 S&P 100 Excess Returns Systematic Trading Mimicking Portfolio FIGURE 9.7 Mimicking Portfolio Returns for the Barclay Systematic Trading Index 195 MLMI Excess Returns Measuring the Long Volatility Strategies of Managed Futures 0.080 0.060 0.040 0.020 0.000 –0.020 –0.040 –0.060 –0.080 –0.175 –0.150 –0.125 –0.100 –0.075 –0.050 –0.025 0.000 0.025 0.050 0.075 0.100 0.125 S&P 100 Excess Returns MLM Index Mimicking Portfolio FIGURE 9.8 Mimicking Portfolio Returns for the MLM Index VALUE AT RISK FOR MANAGED FUTURES The main reason for building mimicking portfolios is to simulate the returns to trend-following strategies for developing risk estimates. Specifically, we can run Monte Carlo simulations with our mimicking portfolios and estimate value at risk (VaR). Armed with these data, we can estimate the probability of the risk of loss associated with long volatility strategies. This is important to help us understand the off-balance sheet risks associated with trend-following strategies.

The mean-variance approach to the portfolio selection problem developed by Markowitz (1952) has been criticized often due to its utilization of variance as a measure of risk exposure when examining the nonnormal returns of CTAs. The value at risk (VaR) measure for financial risk has become accepted as a better measure for investment firms, large banks, and pension funds. As a result of the recurring frequency of down markets since the collapse of Long-Term Capital Management (LTCM) in August 1998, VaR has played a paramount role as a risk management tool and is considered a mainstream technique to estimate a CTA’s exposure to market risk. 377 378 PROGRAM EVALUATION, SELECTION, AND RETURNS With the large acceptance of VaR and, specifically, the modified VaR as a relevant risk management tool, a more suitable portfolio performance measure for CTAs can be formulated in term of the modified Sharpe ratio.1 Using the traditional Sharpe ratio to rank CTAs will underestimate the tail risk and overestimate performance.


pages: 443 words: 51,804

Handbook of Modeling High-Frequency Data in Finance by Frederi G. Viens, Maria C. Mariani, Ionut Florescu

algorithmic trading, asset allocation, automated trading system, backtesting, Bear Stearns, Black-Scholes formula, book value, Brownian motion, business process, buy and hold, continuous integration, corporate governance, discrete time, distributed generation, fear index, financial engineering, fixed income, Flash crash, housing crisis, implied volatility, incomplete markets, linear programming, machine readable, mandelbrot fractal, market friction, market microstructure, martingale, Menlo Park, p-value, pattern recognition, performance metric, power law, principal–agent problem, random walk, risk free rate, risk tolerance, risk/return, short selling, statistical model, stochastic process, stochastic volatility, transaction costs, value at risk, volatility smile, Wiener process

The method allows us to forecast the full pdf of the returns distribution, but for simplicity and concreteness, we focus here on forecasting VaR; other kinds of risk forecasts will be similar. 7.3.1 VALUE AT RISK DEFINITION 7.12 Value at Risk (VaR). Given α ∈ (0, 1), the value at risk at confidence level α for loss L of a security or a portfolio is defined as VaRα (L) = inf {l ∈ R : FL (l) ≥ α}, where FL is the cumulative distribution function of L. In probabilistic terms, VaR is a quantile of the loss distribution. Typical values for α are between 0.95 and 0.995. VaR can also be based on returns instead of losses, in which case α takes a small value such as 0.05 or 0.01. For example, intuitively, a 95% value at risk, VaR0.95 , is a level L such that a loss exceeding L has only a 5% chance of occurring. 7.3.2 DATA AND STYLIZED FACTS Given a set of daily closing prices for some index, we first convert them into negative log returns and then would like to calibrate a skewed t distribution with the EM algorithm.

See also Ordered lower-upper solution pair U-shape, of trade distributions, 42 Utility after retirement, 321 Utility estimations, 287 Utility functions, 296, 299 of power type, 305 Utility loss, 290 Value at risk (VaR), 163, 165, 176. See also VaR entries Value function, 304, 307, 312, 313 for the constant coefficients case, 318 VaR error, 201. See also Value at risk (VaR) VaR estimates, based on Monte Carlo simulation, 199 VaRFixed , 213, 214, 215 VaR forecast(s), 210, 212 high-low frequency, 186 intraday, 202–203 VaR forecasting, 182 VaRHL , 213, 214, 215. See also HL estimator Index Variance, volatility of, 250–252 Variance estimator optimization, 286 Variance forecast, 206 Variance gamma (VG) distributions, 171 Variance-gamma (VG) model, 4, 8–9.

See also CBOE entries calculation of, 98–99 Chronopoulou, Alexandra, xiii, 219 ‘‘Circuit breakers’’, 241 Citi data series, DFA and Hurst methods applied to, 155 City Group, Lévy flight parameter for, 341 Classical risk forecast, 163 Classical time series analysis, 177 Combined Stochastic and Dirichlet problem, 317 Comparative analysis, 239–241 Compensation committees, 53 Compensation policy, 59 Complex models, 23 Compustat North America dataset, 54 Conditional density function, 173 Conditional distribution, 29, 30 Conditional expected returns, 181 Conditional normal distribution, density of, 173 Conditional VaR, 188–189, 207. See also Value at risk (VaR) 423 Conditional variances, 203, 206, 208 of the GARCH(1,1) process, 180 Confidence intervals, for forecasts, 187–188 Consecutive trades, 129 Consensus indicators, 62 Constant coefficient case, 311 Constant default correlation, 79–81 Constant default correlation model, 76 Constant rebalanced portfolio technical analysis (CRP-TA) trading algorithm, 65–66 Constant variance, 181 Constant volatility, 353 Constructed indices, comparison of, 106–107 Constructed volatility index (VIX).


Mathematics for Finance: An Introduction to Financial Engineering by Marek Capinski, Tomasz Zastawniak

Black-Scholes formula, Brownian motion, capital asset pricing model, cellular automata, delta neutral, discounted cash flows, discrete time, diversified portfolio, financial engineering, fixed income, interest rate derivative, interest rate swap, locking in a profit, London Interbank Offered Rate, margin call, martingale, quantitative trading / quantitative finance, random walk, risk free rate, short selling, stochastic process, time value of money, transaction costs, value at risk, Wiener process, zero-coupon bond

Let us reverse the question and fix the probability, 95% say. Now we seek an amount such that the probability of a loss not exceeding this amount is 95%. This is referred to as Value at Risk at 95% confidence level and denoted by VaR. (Other confidence levels can also be used.) So, VaR is an amount such that P (100er − S(1) < VaR) = 95%. It should be noted that the majority of textbooks neglect the time value of money in this context, stating the definition of VaR only for r = 0. Example 9.1 Suppose that the distribution of the stock price is log normal, the logarithmic return k = ln(S(1)/S(0)) having normal distribution with mean m = 12% and standard deviation σ = 30%.

Next, we shall analyse methods of reducing undesirable risk stemming from certain business activities. Our case studies will be concerned with foreign exchange risk. It is possible to deal in a similar way with the risk resulting from unexpected future changes of various market variables such as commodity prices, interest rates or stock prices. We shall introduce a measure of risk called Value at Risk (VaR), which has recently become very popular. Derivative securities will be used to design portfolios with a view to reducing this kind of risk. Finally, we shall consider an application of options to manufacturing a levered investment, for which increased risk will be accompanied by high expected return. 191 192 Mathematics for Finance 9.1 Hedging Option Positions The writer of a European call option is exposed to risk, as the option may end up in the money.

Glossary of Symbols A B β c C C CA CE CE Cov delta div div0 D D DA E E∗ f F gamma Φ k K i m fixed income (risk free) security price; money market account bond price beta factor covariance call price; coupon value covariance matrix American call price European call price discounted European call price covariance Greek parameter delta dividend present value of dividends derivative security price; duration discounted derivative security price price of an American type derivative security expectation risk-neutral expectation futures price; payoff of an option; forward rate forward price; future value; face value Greek parameter gamma cumulative binomial distribution logarithmic return return coupon rate compounding frequency; expected logarithmic return 305 306 Mathematics for Finance M m µ N N k ω Ω p p∗ P PA PE PE PA r rdiv re rF rho ρ S S σ t T τ theta u V Var VaR vega w w W x X y z market portfolio expected returns as a row matrix expected return cumulative normal distribution the number of k-element combinations out of N elements scenario probability space branching probability in a binomial tree risk-neutral probability put price; principal American put price European put price discounted European put price present value factor of an annuity interest rate dividend yield effective rate risk-free return Greek parameter rho correlation risky security (stock) price discounted risky security (stock) price standard deviation; risk; volatility current time maturity time; expiry time; exercise time; delivery time time step Greek parameter theta row matrix with all entries 1 portfolio value; forward contract value, futures contract value variance value at risk Greek parameter vega symmetric random walk; weights in a portfolio weights in a portfolio as a row matrix Wiener process, Brownian motion position in a risky security strike price position in a fixed income (risk free) security; yield of a bond position in a derivative security Index admissible – portfolio 5 – strategy 79, 88 American – call option 147 – derivative security – put option 147 amortised loan 30 annuity 29 arbitrage 7 at the money 169 attainable – portfolio 107 – set 107 183 basis – of a forward contract 128 – of a futures contract 140 basis point 218 bear spread 208 beta factor 121 binomial – distribution 57, 180 – tree model 7, 55, 81, 174, 238 Black–Derman–Toy model 260 Black–Scholes – equation 198 – formula 188 bond – at par 42, 249 – callable 255 – face value 39 – fixed-coupon 255 – floating-coupon 255 – maturity date 39 – stripped 230 – unit 39 – with coupons 41 – zero-coupon 39 Brownian motion 69 bull spread 208 butterfly 208 – reversed 209 call option 13, 181 – American 147 – European 147, 188 callable bond 255 cap 258 Capital Asset Pricing Model 118 capital market line 118 caplet 258 CAPM 118 Central Limit Theorem 70 characteristic line 120 compounding – continuous 32 – discrete 25 – equivalent 36 – periodic 25 – preferable 36 conditional expectation 62 contingent claim 18, 85, 148 – American 183 – European 173 continuous compounding 32 continuous time limit 66 correlation coefficient 99 coupon bond 41 coupon rate 249 307 308 covariance matrix 107 Cox–Ingersoll–Ross model 260 Cox–Ross–Rubinstein formula 181 cum-dividend price 292 delta 174, 192, 193, 197 delta hedging 192 delta neutral portfolio 192 delta-gamma hedging 199 delta-gamma neutral portfolio 198 delta-vega hedging 200 delta-vega neutral portfolio 198 derivative security 18, 85, 253 – American 183 – European 173 discount factor 24, 27, 33 discounted stock price 63 discounted value 24, 27 discrete compounding 25 distribution – binomial 57, 180 – log normal 71, 186 – normal 70, 186 diversifiable risk 122 dividend yield 131 divisibility 4, 74, 76, 87 duration 222 dynamic hedging 226 effective rate 36 efficient – frontier 115 – portfolio 115 equivalent compounding 36 European – call option 147, 181, 188 – derivative security 173 – put option 147, 181, 189 ex-coupon price 248 ex-dividend price 292 exercise – price 13, 147 – time 13, 147 expected return 10, 53, 97, 108 expiry time 147 face value 39 fixed interest 255 fixed-coupon bond 255 flat term structure 229 floating interest 255 floating-coupon bond 255 floor 259 floorlet 259 Mathematics for Finance forward – contract 11, 125 – price 11, 125 – rate 233 fundamental theorem of asset pricing 83, 88 future value 22, 25 futures – contract 134 – price 134 gamma 197 Girsanov theorem 187 Greek parameters 197 growth factor 22, 25, 32 Heath–Jarrow–Morton model hedging – delta 192 – delta-gamma 199 – delta-vega 200 – dynamic 226 in the money 169 initial – forward rate 232 – margin 135 – term structure 229 instantaneous forward rate interest – compounded 25, 32 – fixed 255 – floating 255 – simple 22 – variable 255 interest rate 22 interest rate option 254 interest rate swap 255 261 233 LIBID 232 LIBOR 232 line of best fit 120 liquidity 4, 74, 77, 87 log normal distribution 71, 186 logarithmic return 34, 52 long forward position 11, 125 maintenance margin 135 margin call 135 market portfolio 119 market price of risk 212 marking to market 134 Markowitz bullet 113 martingale 63, 83 Index 309 martingale probability 63, 250 maturity date 39 minimum variance – line 109 – portfolio 108 money market 43, 235 no-arbitrage principle 7, 79, 88 normal distribution 70, 186 option – American 183 – at the money 169 – call 13, 147, 181, 188 – European 173, 181 – in the money 169 – interest rate 254 – intrinsic value 169 – out of the money 169 – payoff 173 – put 18, 147, 181, 189 – time value 170 out of the money 169 par, bond trading at 42, 249 payoff 148, 173 periodic compounding 25 perpetuity 24, 30 portfolio 76, 87 – admissible 5 – attainable 107 – delta neutral 192 – delta-gamma neutral 198 – delta-vega neutral 198 – expected return 108 – market 119 – variance 108 – vega neutral 197 positive part 148 predictable strategy 77, 88 preferable compounding 36 present value 24, 27 principal 22 put option 18, 181 – American 147 – European 147, 189 put-call parity 150 – estimates 153 random interest rates random walk 67 rate – coupon 249 – effective 36 237 – forward 233 – – initial 232 – – instantaneous 233 – of interest 22 – of return 1, 49 – spot 229 regression line 120 residual random variable 121 residual variance 122 return 1, 49 – expected 53 – including dividends 50 – logarithmic 34, 52 reversed butterfly 209 rho 197 risk 10, 91 – diversifiable 122 – market price of 212 – systematic 122 – undiversifiable 122 risk premium 119, 123 risk-neutral – expectation 60, 83 – market 60 – probability 60, 83, 250 scenario 47 security market line 123 self-financing strategy 76, 88 short forward position 11, 125 short rate 235 short selling 5, 74, 77, 87 simple interest 22 spot rate 229 Standard and Poor Index 141 state 238 stochastic calculus 71, 185 stochastic differential equation 71 stock index 141 stock price 47 strategy 76, 87 – admissible 79, 88 – predictable 77, 88 – self-financing 76, 88 – value of 76, 87 strike price 13, 147 stripped bond 230 swap 256 swaption 258 systematic risk 122 term structure 229 theta 197 time value of money 21 310 trinomial tree model Mathematics for Finance 64 underlying 85, 147 undiversifiable risk 122 unit bond 39 value at risk 202 value of a portfolio 2 value of a strategy 76, 87 VaR 202 variable interest 255 Vasiček model 260 vega 197 vega neutral portfolio volatility 71 weights in a portfolio Wiener process 69 yield 216 yield to maturity 229 zero-coupon bond 39 197 94


pages: 350 words: 103,270

The Devil's Derivatives: The Untold Story of the Slick Traders and Hapless Regulators Who Almost Blew Up Wall Street . . . And Are Ready to Do It Again by Nicholas Dunbar

Alan Greenspan, asset-backed security, bank run, banking crisis, Basel III, Bear Stearns, behavioural economics, Black Swan, Black-Scholes formula, bonus culture, book value, break the buck, buy and hold, capital asset pricing model, Carmen Reinhart, Cass Sunstein, collateralized debt obligation, commoditize, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, delayed gratification, diversification, Edmond Halley, facts on the ground, fear index, financial innovation, fixed income, George Akerlof, Glass-Steagall Act, Greenspan put, implied volatility, index fund, interest rate derivative, interest rate swap, Isaac Newton, John Meriwether, junk bonds, Kenneth Rogoff, Kickstarter, Long Term Capital Management, margin call, market bubble, money market fund, Myron Scholes, Nick Leeson, Northern Rock, offshore financial centre, Paul Samuelson, price mechanism, proprietary trading, regulatory arbitrage, rent-seeking, Richard Thaler, risk free rate, risk tolerance, risk/return, Ronald Reagan, Salesforce, Savings and loan crisis, seminal paper, shareholder value, short selling, statistical model, subprime mortgage crisis, The Chicago School, Thomas Bayes, time value of money, too big to fail, transaction costs, value at risk, Vanguard fund, yield curve, zero-sum game

Applying that bottom 5 percent of market outcomes to the bank’s current trading position gave them a number—value at risk (VAR)—which could serve as an assumption to be tested in the market. A day in which the performance was worse than VAR was called an “exception.” If the bank suffered through too many exceptions—a substantially greater fraction of days than one in twenty in which its performance was worse than VAR—then their assumptions about the markets were wrong. Armed with this scientific evidence, senior bankers could then step in and order their traders to cut positions. When Thieke told Fisher about VAR in early 1994, a mystery was suddenly solved.

Because it relied on many simplifying assumptions and was not backed up by empirical evidence, Vasicek’s model was more of a provocative theoretical talking point than a practical, proven tool. And aside from leaning on Merton’s model, it didn’t provide an arbitrage recipe to enforce market pricing. But the invention of value at risk (VAR) in the 1990s provided a huge boost for the idea in practical terms. VAR, remember, was a method for sifting through trading book data to identify the worst that could happen in “normal” conditions—say, on nineteen out of twenty or ninety-nine out of one hundred trading days. At first sight, no one could expect VAR to apply to the opposite extreme: the opaque world of loans, which by definition were not traded, and stayed on a bank’s books until they either were paid back or had defaulted.

And in the process, they used credit default swaps to subvert—and nearly destroy—the financial system. CHAPTER TWO Going to the Mattresses In 1994, a new model for measuring risk—value at risk (VAR)—convinced large segments of the financial world that they were being too cautious in their investing. Another new financial tool, over-the-counter derivatives, seemed to cancel out unwanted risks by transferring them elsewhere. Thanks to VAR and OTC derivatives, the trading positions and profits of banks grew exponentially. In 1998, the fatal flaw of this paradigm was exposed by the collapse of LTCM, but traders and regulators learned the wrong lesson from that near-death experience, setting the financial world up for an even bigger cataclysm.


pages: 130 words: 11,880

Optimization Methods in Finance by Gerard Cornuejols, Reha Tutuncu

asset allocation, call centre, constrained optimization, correlation coefficient, diversification, financial engineering, finite state, fixed income, frictionless, frictionless market, index fund, linear programming, Long Term Capital Management, passive investing, Sharpe ratio, transaction costs, value at risk

Managing risk requires a good understanding of risk which comes from quantitative risk measures that adequately reflect the vulnerabilities of a company. Perhaps the best-known risk measure is Value-at-Risk (VaR) developed by financial engineers at J.P. Morgan. VaR is a measure related to percentiles of loss distributions and represents the predicted maximum loss with a specified probability level (e.g., 95%) over a certain period of time (e.g., one day). Consider, for example, a random variable 3.3. RISK MEASURES: CONDITIONAL VALUE-AT-RISK 37 X that represents loss from an investment portfolio over a fixed period of time. A negative value for X indicates gains. Given a probability level α, α-VaR of the random variable X is given by the following relation: VaRα (X) := min{γ : P (X ≤ γ) ≥ α}

(3.12) f (x,y)≤γ Then, for a given confidence level α, the α-VaR associated with portfolio x is given as VaRα (x) := min{γ ∈ < : Ψ(x, γ) ≥ α}. (3.13) Similarly, we define the α-CVaR associated with portfolio x: CVaRα (x) := 1 Z f (x, y)p(y)dy. 1 − α f (x,y)≥VaRα (x) Note that, 1 Z f (x, y)p(y)dy 1 − α f (x,y)≥VaRα (x) 1 Z ≥ VaRα (x)p(y)dy 1 − α f (x,y)≥VaRα (x) VaRα (x) Z = p(y)dy 1 − α f (x,y)≥VaRα (x) = VaRα (x), CVaRα (x) = (3.14) 3.3. RISK MEASURES: CONDITIONAL VALUE-AT-RISK 39 i.e., CVaR of a portfolio is always at least as big as its VaR. Consequently, portfolios with small CVaR also have small VaR. However, in general minimizing CVaR and VaR are not equivalent.

One well-known modification of VaR is obtained by computing the expected loss given that the loss exceeds VaR. This quantity is often called conditional Value-at-Risk or CVaR. There are several alternative names for this measure in the finance literature including Mean Expected Loss, Mean Shortfall, and Tail VaR. We now describe this risk measure in more detail and discuss how it can be optimized using linear programming techniques when the loss function is linear in the portfolio positions. Our discussion follows parts of articles by Rockafellar and Uryasev [12, 17]. We consider a portfolio of assets with random returns. We denote the portfolio choice vector with x and the random events by the vector y.


pages: 354 words: 26,550

High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems by Irene Aldridge

algorithmic trading, asset allocation, asset-backed security, automated trading system, backtesting, Black Swan, Brownian motion, business cycle, business process, buy and hold, capital asset pricing model, centralized clearinghouse, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, computerized trading, diversification, equity premium, fault tolerance, financial engineering, financial intermediation, fixed income, global macro, high net worth, implied volatility, index arbitrage, information asymmetry, interest rate swap, inventory management, Jim Simons, law of one price, Long Term Capital Management, Louis Bachelier, machine readable, margin call, market friction, market microstructure, martingale, Myron Scholes, New Journalism, p-value, paper trading, performance metric, Performance of Mutual Funds in the Period, pneumatic tube, profit motive, proprietary trading, purchasing power parity, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, Small Order Execution System, statistical arbitrage, statistical model, stochastic process, stochastic volatility, systematic trading, tail risk, trade route, transaction costs, value at risk, yield curve, zero-sum game

N − N1 MDi j is the k=1 average maximum drawdown. 1/2 N 2 MDi j is a type k=1 of variance below the N th largest drawdown; accounts for very large losses. Value-at-risk–based measures. Value at risk (VaRi ) describes the possible loss of an investment, which is not exceeded with a given probability of 1 − α in a certain period. For normally distributed returns, VaRi = −(E [ri ] + zα σi ), where zα is the α-quantile of the standard normal distribution. (Continued) 54 HIGH-FREQUENCY TRADING TABLE 5.1 (Continued) E [r]−r f Excess return on value at risk (Dowd, [2000]) Excess R on VaR = Conditional Sharpe ratio (Agarwal and Naik [2004]) Conditional Sharpe = Modified Sharpe ratio (Gregoriou and Gueyie [2003]) Modified Sharpe = VaRi E [r]−r f C VaRi CVaRi = E [−rit |rit ≤ −VaRi ] E [r]−r f M VaRi Cornish-Fisher expansion is calculated as follows: Not suitable for non-normal returns.

Bangia et al. (1999), for example, document that liquidity risk accounted for 17 percent of the market risk in long USD/THB positions in May 1997, and Le Saout (2002) estimates that liquidity risk can reach over 50 percent of total risk on selected securities in CAC40 stocks. 264 HIGH-FREQUENCY TRADING Bervas (2006) proposes the following liquidity-adjusted VaR measure: (17.7) VaR L = VaR + Liquidity Adjustment = VaR − µ S + zα σ S where VaR is the market risk value-at-risk discussed previously in this chapter, µS is the mean expected bid-ask spread, σ S is the standard deviation of the bid-ask spread, and zα is the confidence coefficient corresponding to the desired α– percent of the VaR estimation. Both µS and σ S can be estimated either from raw spread data or from the Roll (1984) model. Using Kyle’s λ measure, the VaR liquidity adjustment can be similarly computed through estimation of the mean and standard deviation of the trade volume: VaR L = VaR + Liquidity Adjustment = VaR − α̂ + λ̂(µNVOL + zα σ NVOL ) (17.8) where α̂ and λ̂ are estimated using OLS regression following Kyle (1985): Pt = α + λNVOLt + εt (17.9) Pt is the change in market price due to market impact of orders, and NVOLt is the difference between the buy and sell market depths in period t.

Finally, the Upside Potential ratio, produced by Sortino, van der Meer, and Plantinga (1999), measures the average return above the benchmark (the first higher partial moment) per unit of standard deviation of returns below the benchmark. Value-at-risk (VaR) measures also gained considerable popularity as metrics able to summarize the tail risk in a convenient point format within a statistical framework. The VaR measure essentially identifies the 90 percent, 95 percent, or 99 percent Z-score cutoff in distribution of returns (the metric is also often used on real dollar distributions of daily profit and loss). VaR companion measure, the conditional VaR (CVaR), also known as expected loss (EL), measures the average value of return within the cut-off tail. Of course, the original VaR assumes normal distributions of returns, whereas the returns are known to be fat-tailed.


Monte Carlo Simulation and Finance by Don L. McLeish

algorithmic bias, Black-Scholes formula, Brownian motion, capital asset pricing model, compound rate of return, discrete time, distributed generation, finite state, frictionless, frictionless market, implied volatility, incomplete markets, invention of the printing press, martingale, p-value, random walk, risk free rate, Sharpe ratio, short selling, stochastic process, stochastic volatility, survivorship bias, the market place, transaction costs, value at risk, Wiener process, zero-coupon bond, zero-sum game

VARIANCE REDUCTION TECHNIQUES independent with probability density function f (x) = c(1 + (x/b)2 )−2 (the re-scaled student distribution with 3 degrees of freedom). We wish to esP timate a weekly Value at Risk, V ar.95 , a value ev such that P [ 5i=1 Xi < v] = 0.95. If we wish to do this by simulation, suggest an appropriate method involving importance sampling. Implement and estimate the variance reduction. 10. Suppose three different simulation estimators Y1 , Y2 , Y3 have means which depend on two unknown parameters θ1 , θ2 so that Y1 , Y2 , Y3 , are unbiased estimators of θ1 , θ1 + θ2 , θ2 respectively. Assume that var(Yi ) = 1, cov(Yi , Yj ) = −1/2 an we want to estimate the parameter θ1 . Should we use only the estimator Y1 which is the unbiased estimator of θ1 , or some linear combination of Y1 , Y2 , Y3 ?

By part (c), var(θ̂) = var(T1 ) + var(ε) and var(T1 ) = var(θ̂) − var(ε) < var(θ̂). In other words, for any variable Z, E[θ̂|Z] has the same expectation as does θ̂ but smaller variance and the decrease in variance is largest if Z and θ̂ are nearly independent, because in this case E[θ̂|Z] is close to a constant and its variance close to zero. In general the search for an appropriate Z so as to reducing the variance of an estimator by conditioning requires searching for a random variable Z such that: 1. the conditional expectation E[θ̂|Z] with the original estimator is computable 2. var(E[θ̂|Z]) is substantially smaller than var(θ̂). 250 CHAPTER 4.

It is easy to show once again that the estimator θ̂st is an unbiased estimator of θ, since E(θ̂st ) = aEf (V1 ) + (1 − a)Ef (V2 ) Z a Z 1 1 1 =a f (x) dx + (1 − a) f (x) dx a 1 − a 0 a Z 1 f (x)dx. = 0 Moreover, var(θ̂st ) = a2 var[f (V 1 )] + (1 − a)2 var[f (V 2 )] + 2a(1 − a)cov[f (V 1 ), f (V 2 )]. (4.10) Even when V1 , V2 are independent, so we obtain var(θ̂st ) = a2 var[f (V1 )] + (1 − a)2 var[f (V2 )], there may be a dramatic improvement in variance over crude Monte Carlo provided that the variability of f in each of the intervals [0, a] and [a, 1] is substantially less than in the whole interval [0, 1].


pages: 312 words: 35,664

The Mathematics of Banking and Finance by Dennis W. Cox, Michael A. A. Cox

backpropagation, barriers to entry, Brownian motion, call centre, correlation coefficient, fixed income, G4S, inventory management, iterative process, linear programming, meta-analysis, Monty Hall problem, pattern recognition, random walk, traveling salesman, value at risk

When looking at exposure to two or more risks – e.g. the risk in a portfolio of two assets, say gold and euros – the risk measures must take account of the likely joint movements (or ‘correlations’) in the asset prices as well as the risks in the individual instruments. This can be written as: VaR = VaR21 + VaR22 + 2ρ12 VaR1 VaR2 where VaR1 is the value at risk arising from the first risk factor, VaR2 is the value at risk arising from the second risk factor, and ρ12 is the correlation between movements in the two risk factors. Given the definition of VaR above, this can be written as: VaR = x12 σ12 + x22 σ22 + 2ρ12 x1 σ1 x2 σ2 Value at Risk 265 where σ1 and σ2 are the confidence level volatilities for the two risk factors (equivalently σ12 and σ22 are the confidence level variances) and ρ12 is the correlation between movements in the two risk factors.

The functions are shown in Figures 27.11, 27.12 and 27.13 for σ = 2 and μ = 2, 4 and 6. 28 Value at Risk 28.1 INTRODUCTION Value at risk, or VaR is an attempt to estimate the greatest loss likely if a defined risk were to occur. For example, it could represent the loss in value of a portfolio of shares were a market slump to occur. Typically the way this works in practice is that the analyst calculates the greatest loss that would arise in 99% of all cases. That means that in 99% of cases the loss would actually exceed the amount of the calculated VaR, which is effectively a boundary value. This really is just a probabilistic statement. If a VaR is estimated to be £1 million with a 99% confidence, or a probability of 0.99, then a loss of more than £1 million might be expected on one day in every 100.

The underlying data has an accuracy, which is certainly lower than 99% (i.e. there is only a 1% chance that the data will be unrepresentative or that the distribution selected will be inappropriate). Given the difficulties inherent with data fitting and the underlying data integrity problem, a much lower level of accuracy is actually achieved – perhaps only 80%. 28.3 CALCULATING VALUE AT RISK Value at risk for a single position is calculated as: VaR = Sensitivity of position to change in market prices × Estimated change in price or VaR = Amount of the position × Volatility of the position = xσ where x is the position size and volatility, σ , is the proportion of the value of the position which may be lost over a given time horizon at the specified confidence level.


pages: 318 words: 99,524

Why Aren't They Shouting?: A Banker’s Tale of Change, Computers and Perpetual Crisis by Kevin Rodgers

Alan Greenspan, algorithmic trading, bank run, banking crisis, Basel III, Bear Stearns, Berlin Wall, Big bang: deregulation of the City of London, bitcoin, Black Monday: stock market crash in 1987, Black-Scholes formula, buy and hold, buy low sell high, call centre, capital asset pricing model, collapse of Lehman Brothers, Credit Default Swap, currency peg, currency risk, diversification, Fall of the Berlin Wall, financial innovation, Financial Instability Hypothesis, fixed income, Flash crash, Francis Fukuyama: the end of history, Glass-Steagall Act, Hyman Minsky, implied volatility, index fund, interest rate derivative, interest rate swap, invisible hand, John Meriwether, latency arbitrage, law of one price, light touch regulation, London Interbank Offered Rate, Long Term Capital Management, Minsky moment, money market fund, Myron Scholes, Northern Rock, Panopticon Jeremy Bentham, Ponzi scheme, prisoner's dilemma, proprietary trading, quantitative easing, race to the bottom, risk tolerance, risk-adjusted returns, Silicon Valley, systems thinking, technology bubble, The Myth of the Rational Market, The Wisdom of Crowds, Tobin tax, too big to fail, value at risk, vertical integration, Y2K, zero-coupon bond, zero-sum game

Each day every department’s risk system automatically sent a report to the Engine with a compendium of that department’s Greeks. From all this information, which was like a risk report for the entire bank, the Engine then tried to figure out how much money the bank could potentially lose if things went wrong. The amount was called ‘value at risk’, or VaR, and it was used to replace the concept of ‘maximum potential loss’ within the RAROC process. But instead of representing maximum loss, VaR represented something subtly – but crucially – different. It was a statistical measure that was meant to show the amount that the bank could lose that would only be exceeded 1 per cent of the time. ‘It’s like you go to the doctor with a stubbed toe and ask him, “Doc, tell me the worst”,’ Darren once joked, ‘and he says, “Well, there’s 99 per cent chance of it being better than having both your legs amputated.”’

(Frankie Valli and the Four Seasons), 3–5, 9 oil, 139, 148, 149–50, 220 Oktoberfest, 144–5 one-stop shop, 145, 160, 168, 230 online banking, 233–4 OPTICS, 101, 103, 131, 153, 170 option-based recapitalisation charge (OBRC), 217–18 options, 10, 13, 20, 23, 27, 33–4, 38, 43–7, 49, 77, 91–114, 121, 128, 134, 137, 140, 141, 149, 150, 218, 222–3 Asian options, 106 barrier products, 104, 107, 108 carry trade, 108–10 commoditisation, 110–11 delta hedging, 97 double knockouts, 107 exotics, 105–11, 127 foreign exchange, 10, 13, 20, 23, 27, 33–4, 38, 43–7, 49, 77, 95–114, 222–3 gamma, 97–8, 100 Greeks, 98, 99, 101, 105, 107, 110, 111, 112, 114, 127, 129, 150, 157, 158, 182, 219 lattice methods, 112 lookbacks, 107 over-the-counter (OTC) market, 96, 209 power options, 107 pricing of, 91–5, 107, 111–14, 128, 133, 150 range trades, 107, 108 rho, 98 risk-weighted assets (RWA), 124–31, 134, 166, 211 spoofing, 99, 192–3 strike price, 94, 95, 104, 113, 218 theta, 98, 140 time decay, 98 tree approach, 112–14 vega, 98 volatility, 94, 98, 128–9 Organisation for Economic Co-operation and Development (OECD), 124–5, 139, 207 over-the-counter (OTC) market, 96, 209 P Panopticon, 199 Patton, George Smith, 84 pay, 43, 161–3, 210, 216 PayPal, 233 peer-to-peer, 234 pension funds, 9, 61, 76, 96 Philippines, 137 ‘pipes’, 71, 72 ‘pips’, 13, 18, 41, 65, 73, 77 Piranha, 48 Pittsburgh, Pennsylvania, 209 Plankton Strategy, 38, 40, 55, 58 Poland, 117, 167 Ponzi schemes, 205–6 Portugal, 173 power options, 107 Prebon Yamane, 28 Precision Pricing, 65 prime brokerage (PB), 61–3, 66, 120, 209 principal model, 7–8 principles-based regulation (PBR), 201–2 Prisoners’ Dilemma, 84, 186, 197, 201 Procter and Gamble, 109, 136, 155 production credits, 49, 50 proprietary trading, 22, 31, 39, 46–7, 125 Prosper, 234 Pushkin, Alexander, 30 Q quadratic equations, 105–6 quantitative analysis, xiv, 100–1, 103–8, 110–14, 126, 150, 158 quantitative easing, 82 R Rand, Ayn, 202 range trades, 107 ratings agencies, 155–8, 208, 219 rational markets, 202–6 recapitalisation, 217 recovery value, 151, 157, 160 regulation, 73, 80, 131, 148, 152, 168, 174, 180, 186–7, 191–6, 198–9, 200–18, 229, 231, 232 BaFin, 200, 202 bank tax, 216 Basel Accords, 124, 125, 130, 166, 207–9, 211, 217, 231 Big Bang (1986), 201 Central Counterparties (CCPs), 209, 213–15, 229 Commodity Futures Trading Commission (CFTC), 202 computers, 209, 213 ‘cops on the beat’, 215 Dodd–Frank Act (2010), 209, 230 European Banking Authority (EBA), 211–12 Financial Services (Banking Reform) Act (2013), 210 Financial Stability Board (FSB), 212, 214, 216 Glass–Steagall Act (1933), 168, 201, 230 Gramm–Leach–Bliley Act (1999), 168, 201 option-based recapitalisation charge (OBRC), 217–18 principles-based regulation (PBR), 201–2 rational markets, 6, 202–6 Riegle–Neal Act (1994), 168 shadow banking, 214–15 surveillance, 110, 190, 195–9 trade repositories, 209–10 relative value trades, 72–3 repo markets, 171 request for quote (RFQ), 50, 51 retail banking, 169, 229–30, 233 retail FX, retail aggregators, 21, 61, 66, 74, 75, 79, 82–3 Reuters, 9, 22–3, 188 Reuters Dealing machines, 23, 25–8, 31, 32, 50, 59, 73 Revolutionary Application Program Interface Development (RAPID), 56–9, 77, 101 rho, 98 Riegle–Neal Act (1994), 168 risk, risk management, 15, 16, 19, 20–1, 24, 29, 31, 38–9, 40, 44–5, 53–5, 56–9, 64, 67, 70–1, 72, 73, 76, 77, 79, 99, 101–8, 110–14, 121–31, 135–6, 139, 142, 144, 150, 152, 157–63, 166– 7, 169–70, 172, 174, 200–1, 206–7, 209, 213, 219, 221, 224–7, 233 agency-like approach, 119, 127 Automated Risk Manager (ARM), 53–5, 56–9, 64, 67, 70–1, 72, 73, 76, 79, 101, 129, 169 BTAnalytics, 107, 108, 112, 134, 150 collateralised debt obligations (CDOs), 154, 156, 158–63 complex risk, 159 concentration, 213 counterparty credit risk, 141–2, 172, 201, 202–3, 209 credit default swaps (CDSs), 151–3, 157, 158, 164, 225 credit risk, 7, 8, 62–3, 123, 125–6, 130, 141, 151, 209 DBAnalytics, 150, 153 foreign exchange, 15, 16, 19, 20–1, 24, 29, 31, 38, 39, 40, 44–5, 49, 51, 53, 62, 77, 192 market risk, 7, 62, 123, 126, 130, 167 model dopes, 221–2 OPTICS, 101, 103, 131, 153, 170 perception gap, 220, 224 quantitative analysis, xiv, 100–1, 103–8, 110–14, 126, 150, 158 Revolutionary Application Program Interface Development (RAPID), 56–9, 77, 101 risk-adjusted return on capital (RAROC), 126–7, 131, 144, 219 risk-weighted assets (RWA), 124–31, 134, 166, 208, 211 shares, 92 Spreadsheet Solutions Framework (SSF), 111, 121, 138, 153 value at risk (VaR), 127–31, 135–6, 139, 142, 157, 158, 169, 170, 172, 174, 204, 207, 219, 226, 227 RiskMetrics, 131 rogue systems, 79–80 Rolling Stones, 87, 89, 151, 171, 172 Royal Bank of Scotland (RBS), 48, 217, 233 ‘Rule 575 – Disruptive Practices Prohibited’, 193 Russia, 30, 33, 38, 76, 114–32, 137–42, 146, 148, 150, 151, 152, 163, 172, 175, 199, 202, 204, 207, 221, 224, 228 Financial Crisis (1998), xi–xii, 29, 114, 115–16, 118–22, 124, 137–43, 146, 163, 172, 175, 202, 207, 221, 224, 228 rouble, 115–16, 118–22, 131, 137, 138, 139 rouble-denominated bonds (GKOs), 118–32, 134, 138–43, 149, 152, 158, 159, 173 S sales-traders, 50, 78 salespeople, 8–9, 11, 13–15, 20, 24, 28, 29, 33, 35, 37, 46, 47–52, 68, 96, 120 Salomon Brothers, 122, 133–4 Sanford, Charlie, 35, 126 Sarao, Navinder Singh, 193 Scholes, Myron, 94–5, 97 screen scraping, 32–3, 50, 59 Securities and Exchange Commission, 215 securities, 91, 145, 155 September 11 attacks (2001), 52, 146, 147 settlements, 5, 24, 40, 44, 51, 61, 101, 209 shadow banking, 214–15 shareholders, 25, 35, 47, 73, 84, 90, 124, 169, 172, 205 shares, 50, 60, 91, 113, 128, 212, 217 forward trades, 91–4, 133, 152 recapitalisation, 217 tree approach, 113 volatility, 94, 128–9 short-dated deposits, 8 Silicon Valley, 234 Singapore, 37, 137, 211 skewed prices, 18, 19, 73, 96, 98 slippage, 188–9 smartphones, 233 Soros, George, 24 South Africa, xvi Soviet Union (1922–91), 22, 64, 199, 204 Spain 87–9, 151, 159, 171, 172 ‘special purpose vehicle’, 154 spoofing, 99, 192–3 spot, 3–28, 33–4, 42–4, 46, 48–55, 67, 76, 77, 91, 96, 97, 98–9, 108, 111, 118, 122, 159, 165, 169, 188, 189, 199, 201, 222 automation, 23–8, 31–3, 42, 48–55, 56–84, 111, 199, 201 brokers, 3–28, 33, 43, 63, 192 traders, 3–31, 33, 35, 38, 41, 43, 46, 50, 52–4, 67–8, 73, 76, 78, 96, 99, 122, 189 voice traders, 54, 67, 76, 122 spread, 12, 14–15, 16, 18, 19, 31, 41–2, 44, 45, 53, 55, 61, 64, 68, 69, 75, 80, 96, 225 spread crossing, 18, 19 Spreadsheet Solutions Framework (SSF), 111, 121, 138, 153 spreadsheets, 33, 38, 40, 107, 111, 219 Standard and Poor’s (S&P), 155, 157, 160 Stanford University, 211 stocks, 50, 60, 79, 89, 117, 129, 133, 136, 138, 147–8, 203–4 indexes, 147–8 streamlining, 230–2 Stress VaR, 207, 211 strike price, 94, 95, 104, 113, 218 structured products, 44 structuring, 153, 219 student loans, 155, 231 sub-prime mortgages, 74, 88–90, 159–61, 170–2, 207, 227 suits, 132, 140–1, 149 surveillance, 110, 190, 195–9 swaps, 8, 92, 119–20, 121, 125, 126, 132, 134, 136, 141, 148, 149, 152, 173 Sweden, 148, 167 SWIFT, 23 Switzerland, 10, 81–3, 120, 211 franc, 9, 20, 31, 81–3 Swiss National Bank, 81–3 T Telerate, 9 ‘ten sigma event’, 135 Thailand, 135, 136, 139 theta, 98 ‘Things Can Only Get Better’ (D:Ream), 120, 121 time decay, 98 Tobin Tax, 216 Tokyo, Japan, 72 Tolstoy, Leo, 30 ‘too big to fail’, 90, 217 total return swaps (TRSs), 119–20, 136, 152 tracker funds, 147–8 trade repositories, 209–10 tranches, 154–9, 161, 174, 208 transparency, 141, 143, 151, 170, 212, 219 ‘tree approach’, 112–14 ‘trial by meeting’, 45, 52 triangle arbitrage, 31–2, 42, 54, 91, 122 Triangle Man, 31–2, 42, 54, 91, 122 Triple I High Risk Opportunities Fund, 116, 141 Troubled Asset Relief Program (TARP), 174, 175 Truth and Reconciliation, xvi Turkey, 231 Tuscany, Italy, 78, 115–16, 141 two-way pricing, 11–21, 23, 99 U UBS, 48, 52, 55, 59, 65, 68, 167, 197, 227 Ulster Bank, 233 United Kingdom, 22, 48, 89, 117, 120, 145, 163, 190, 200, 201–2, 203, 210, 233–4 Big Bang (1986), 201 Exchange Rate Mechanism Crisis (1992), xi, 22, 102–3 Financial Services Act (2013), 210 Flash Crash (2010), 79–80, 193 FX-fixing scandal (2013), 190 general election (1997), 120 LIBOR scandal (2012), 181–7, 188, 189, 190, 197, 198 London, England, vii–x, xi, 3, 9, 15, 35, 69, 72, 76, 79, 91, 120, 121, 137, 140, 193, 199, 202, 209 Northern Rock crisis (2007), 89, 163 pound, 9, 13, 28, 53, 184 principles-based regulation (PBR), 201–2 RBS recapitalisation (2008), 217 RBS/Ulster Bank system failure (2012), 233 Vickers Report (2013), 211, 230 United States, vii, xi, 9, 52, 56, 64, 69, 72, 74, 88–90, 108, 120, 143, 159–60, 168, 170–2, 196, 200, 203–6, 207, 209, 210, 212 Black Monday (1987), 108, 138, 204, 207 Comprehensive Capital Analysis and Review (CCAR), 212 Congress, 202, 205 Dodd–Frank Act (2010), 209, 230 dollar, 9, 12, 14, 17–18, 23, 28, 31, 53, 69, 71–2, 73, 79, 88, 96, 97, 99, 100, 104, 118, 119, 135, 136, 140, 184–6, 189 Federal Reserve, xii, 90, 109, 143, 187, 202–6, 212 Flash Crash (2010), 79–80, 193 Glass–Steagall Act (1933), 168, 201, 230 Gramm–Leach–Bliley Act (1999), 168, 201 Lehman Brothers bankruptcy (2008), 75, 89, 172–4, 179, 180, 217, 226, 230, 232 New York, 9, 35, 71, 72, 79, 88, 109, 169, 202 Riegle–Neal Act (1994), 168 September 11 attacks (2001), 52, 146, 147 Subprime Crisis (2007), 74, 88–90, 160–1, 170–2, 207, 227 Treasury bills, 119, 129, 133, 147 Vietnam War (1955–75), 181 Wall Street Crash (1929), 168 universal banking model, 230 V Valli, Frankie, 3–5, 9 value at risk (VaR), 127–31, 135–6, 139, 142, 157, 158, 169, 170, 172, 174, 204, 207, 219, 226, 227 Stress VaR, 207, 211 variation margin, 120, 141 vega, 98 Vickers Report (2013), 211, 230 voice traders, 54, 67, 76, 122 volatility, 14, 26, 46–7, 74–5, 80, 94, 98, 128–9, 137 Volker, Paul, 230 W Wall Street Crash (1929), 168 ‘Watanabe, Mrs’, 61, 75, 79, 82 weather derivatives, 144–5, 146 Wendt, Froukelien, 214 West Texas Intermediate, 139 Wheatley Report (2012), 184, 188 ‘window, the’ 118, 137, 188 ‘wisdom of crowds’, 204 WM/R, 187–8 World Cup 1998 France, 140 2014 Brazil, 195 World War I (1914–18), 207 X XTX, 233 Y Yale University, 110 ‘yours-and-mine’, 15–16, 18, 19, 192 Z zero coupon bonds, 118–32, 134, 138–43, 149, 152, 158, 159, 173 Zombanakis, Minos, 181–2 Zopa, 234 This ebook is copyright material and must not be copied, reproduced, transferred, distributed, leased, licensed or publicly performed or used in any way except as specifically permitted in writing by the publishers, as allowed under the terms and conditions under which it was purchased or as strictly permitted by applicable copyright law.

two-way pricing To provide a bid and an offer simultaneously for the same financial product at the request of a client. universal bank A bank that comprises divisions that offer the services of retail, commercial and investment banks under one corporate umbrella. Examples: Deutsche Bank, Citigroup, JPMorgan, HSBC, RBS. VaR Value at risk: a modelled measure of the risk of a portfolio of assets and liabilities that states the estimated maximum loss given a certain probability of the occurrence of that loss (say, 1 per cent) over a defined time period (say, one day). voice trader A trader who prices deals and manages positions himself and who isn’t the meat-based servant to the demands of an electronic trading system.


pages: 313 words: 34,042

Tools for Computational Finance by Rüdiger Seydel

bioinformatics, Black-Scholes formula, Brownian motion, commoditize, continuous integration, discrete time, financial engineering, implied volatility, incomplete markets, interest rate swap, linear programming, London Interbank Offered Rate, mandelbrot fractal, martingale, random walk, risk free rate, stochastic process, stochastic volatility, transaction costs, value at risk, volatility smile, Wiener process, zero-coupon bond

The normal distribution has kurtosis 3. So the excess kurtoris is the difference to 3. Frequently, data of returns are characterized by large values of excess kurtosis. 8 52 Chapter 1 Modeling Tools for Financial Options x and a constant α > 0. A correct modeling of the tails is an integral basis for value at risk (VaR) calculations. For the risk aspect compare [BaN97], [Dowd98], [EKM97], [ArDEH99]. For distributions that match empirical data see [EK95], [Shi99], [BP00], [MRGS00], [BTT00]. Estimates of future values of the volatility are obtained by (G)ARCH methods, which work with different weights of the returns [Shi99], [Hull00], [Tsay02], [FHH04], [Rup04].

By taking the average (3.21) VAV := 12 V + V − (AV for antithetic variate) we obtain a new approximation, which in many cases is more accurate than V . Since V and VAV are random variables we can only aim at Var(VAV ) < Var(V ) . In view of the properties of variance and covariance (equation (B1.7) in Appendix B1) we have Var(VAV ) = 14 Var(V + V − ) = 14 Var(V ) + 14 Var(V − ) + 12 Cov(V , V − ). From |Cov(X, Y )| ≤ (3.22) 1 [Var(X) + Var(Y )] 2 (follows from (B1.7)) we deduce Var(VAV ) ≤ 1 (Var(V ) + Var(V − )). 2 This shows that in the worst case only the efficiency is slightly deteriorated by the additional calculation of V − . The favorable situation is when the covariance is negative.

So we expect relatively large values of Cov(V, V ∗ ) or Cov(V , V ∗ ), close to its upper bound, 1 1 Cov(V , V ∗ ) ≈ Var(V ) + Var(V ∗ ). 2 2 This leads us to define “closeness” between the options as sufficiently large covariance in the sense Cov(V , V ∗ ) > 1 Var(V ∗ ). 2 (3.23) The method is motivated by the assumption that the unknown error V −V has the same order of magnitude as the known error V ∗ −V ∗ . This expectation can be written V ≈ V +(V ∗ − V ∗ ), which leads to define as another approximation VCV := V + V ∗ − V ∗ (3.24) (CV for control variate). We see from (B1.6) (with β = V ∗ ) and (B1.7) that Var(VCV ) = Var(V − V ∗ ) = Var(V ) + Var(V ∗ ) − 2Cov(V , V ∗ ). 3.5 Monte Carlo Simulation 111 4.7 4.65 4.6 4.55 4.5 4.45 4.4 4.35 4.3 4.25 4.2 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 Fig. 3.6.


pages: 408 words: 85,118

Python for Finance by Yuxing Yan

asset-backed security, book value, business cycle, business intelligence, capital asset pricing model, constrained optimization, correlation coefficient, data science, distributed generation, diversified portfolio, financial engineering, functional programming, implied volatility, market microstructure, P = NP, p-value, quantitative trading / quantitative finance, risk free rate, Sharpe ratio, tail risk, time value of money, value at risk, volatility smile, zero-sum game

See TORQ database trading strategies about 251 bear spread with calls 251 bear spread with puts 251 bull spread with calls 251 bull spread with puts 251 butterfly with calls 251, 256, 257 butterfly with puts 251 calendar spread 251, 254 [ 384 ] covered call 252 straddle 251-254 strangle 251 strap 251 strip 251 trading volume and closing price, viewing 156 trytond_account_statement module 91 trytond_currency module 91 trytond_project module 91 trytond_stock_forecast module 91 trytond_stock_split module 91 T-test about 193 equal means test, performing 194 equal variances test, performing 194 January effect, testing 195 performing 193, 194 ttest_1samp() function 111 tuple data type 39, 40 two strings combining 37 two-year price movement graphical representation 153, 154 type() function 36 spread (1984) 197, 198 V U uniform distribution random numbers, generating from 312, 313 unique() function 228, 317 Up-and-in option 337 up-and-in parity graphical representation 340-342 Up-and-out option 337 up-and-out parity graphical representation 340-342 upper() function 37, 38 U.S. Department of the Treasury URL 174 useful applications 52-week high and low trading strategy 196 Amihud's model for illiquidity (2002) 198, 199 Pastor and Stambaugh (2003) liquidity measure 199- 201 Roll's model to estimate Value at Risk. See VaR values assigning, to variables 24 vanilla options 335 VaR using 210, 211 variable deleting 27 initializing 17 unsigning 27 values, assigning to 24 values, displaying 24 variance-covariance matrix estimating 212, 214 optimization 214, 215 versions, Python finding 21 Visual financial statements URL 163 volatility about 348, 360 over two periods, equivalency testing 354 versus return, comparing 161, 162 volatility clustering 362, 363 volatility skewness 360, 362 volatility smile 360, 362 W web page data, retrieving from 180, 181 web page examples URL 163 while loop about 284 used, for estimating implied volatility 286, 287 X xlim() function 130 x.sum() dot function 107 [ 385 ] Y Yahoo!

For example, instead of showing how to run CAPM to estimate the beta (market risk), I show you how to estimate IBM, Apple, or Walmart's betas. Rather than just presenting formulae that shows you how to estimate a portfolio's return and risk, the Python programs are given to download real-world data, form various portfolios, and then estimate their returns and risk including Value at Risk (VaR). When I was a doctoral student, I learned the basic concept of volatility smiles. However, until writing this book, I had a chance to download real-world data to draw IBM's volatility smile. [2] Preface What this book covers Chapter 1, Introduction and Installation of Python, offers a short introduction, and explains how to install Python and covers other related issues such as how to launch and quit Python.

Finance, Google Finance, Federal Reserve Data Library, and Prof. French's Data Library. Then, we discussed various statistical tests, such as T-test, F-test, and normality test. In addition, we presented Python programs to run capital asset pricing model (CAPM), run a Fama-French three-factor model, estimate the Roll (1984) spread, estimate Value at Risk (VaR) for individual stocks, and also estimate the Amihud (2002) illiquidity measure, and the Pastor and Stambaugh (2003) liquidity measure for portfolios. For the issue of anomaly in finance, we tested the existence of the socalled January effect. For high-frequency data, we explained briefly how to draw intra-day price movement and retrieved data from the Trade, Order, Report and Quote (TORQ) database and the Trade and Quote (TAQ) database.


pages: 545 words: 137,789

How Markets Fail: The Logic of Economic Calamities by John Cassidy

Abraham Wald, Alan Greenspan, Albert Einstein, An Inconvenient Truth, Andrei Shleifer, anti-communist, AOL-Time Warner, asset allocation, asset-backed security, availability heuristic, bank run, banking crisis, Bear Stearns, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Black Monday: stock market crash in 1987, Black-Scholes formula, Blythe Masters, book value, Bretton Woods, British Empire, business cycle, capital asset pricing model, carbon tax, Carl Icahn, centralized clearinghouse, collateralized debt obligation, Columbine, conceptual framework, Corn Laws, corporate raider, correlation coefficient, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, Daniel Kahneman / Amos Tversky, debt deflation, different worldview, diversification, Elliott wave, Eugene Fama: efficient market hypothesis, financial deregulation, financial engineering, financial innovation, Financial Instability Hypothesis, financial intermediation, full employment, Garrett Hardin, George Akerlof, Glass-Steagall Act, global supply chain, Gunnar Myrdal, Haight Ashbury, hiring and firing, Hyman Minsky, income per capita, incomplete markets, index fund, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), invisible hand, John Nash: game theory, John von Neumann, Joseph Schumpeter, junk bonds, Kenneth Arrow, Kickstarter, laissez-faire capitalism, Landlord’s Game, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, Louis Bachelier, low interest rates, mandelbrot fractal, margin call, market bubble, market clearing, mental accounting, Mikhail Gorbachev, military-industrial complex, Minsky moment, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, Myron Scholes, Naomi Klein, negative equity, Network effects, Nick Leeson, Nixon triggered the end of the Bretton Woods system, Northern Rock, paradox of thrift, Pareto efficiency, Paul Samuelson, Phillips curve, Ponzi scheme, precautionary principle, price discrimination, price stability, principal–agent problem, profit maximization, proprietary trading, quantitative trading / quantitative finance, race to the bottom, Ralph Nader, RAND corporation, random walk, Renaissance Technologies, rent control, Richard Thaler, risk tolerance, risk-adjusted returns, road to serfdom, Robert Shiller, Robert Solow, Ronald Coase, Ronald Reagan, Savings and loan crisis, shareholder value, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical model, subprime mortgage crisis, tail risk, Tax Reform Act of 1986, technology bubble, The Chicago School, The Great Moderation, The Market for Lemons, The Wealth of Nations by Adam Smith, too big to fail, Tragedy of the Commons, transaction costs, Two Sigma, unorthodox policies, value at risk, Vanguard fund, Vilfredo Pareto, wealth creators, zero-sum game

For example, when a bank sells some Treasury bonds and buys some volatile technology stocks, its VAR rises by a certain amount, say $10 million, giving its management a precise read on how much extra risk it has taken on. “In contrast with traditional risk measures, VaR provides an aggregate view of a portfolio’s risk that accounts for leverage, correlations, and current positions,” Philippe Jorion, a professor of finance at the University of California, Irvine, wrote in his 1996 book, Value at Risk: The New Benchmark for Controlling Market Risk, which helped to popularize the methodology. “As a result, it is truly a forward looking risk measure.” According to Wall Street folklore, the concept of value-at-risk originated in the late 1980s, when, following the stock market crash of 1987, the late Sir Dennis Weatherstone, J.P.

Steel Corporation utilitarian philosophy utopian economics Greenspan and Keynes’s attack on reality-based economics versus triumph of market failures and; see also general equilibrium theory invisible hand rational expectations theory specific economists Value at Risk (Jorion) value-at-risk (VAR) models Vanguard Group Versailles, Treaty of Victoria, Queen of England Vienna, University of Vienna Circle Viniar, David Vinik, Jeffrey Volcker, Paul Voltaire von Neumann, John Wachovia Bank Wald, Abraham Wallace, Neil Wall Street Journal, The Wal-Mart Walras, Léon Walters, Alan Warren, Elizabeth Washington, George Washington Mutual Washington University School of Business Waxman, Henry Wealth of Nations, The (Smith) Weatherstone, Dennis Webvan Weill, Sanford “Sandy” Welch, Ivo Welch, Jack Wellesley College Wells Fargo Bank White, William White House Council of Economic Advisers Whither Socialism (Sitglitz) Whitney, Eli Williams, John D.

“It’s what our clients pay us to do, and as you all know, we’re pretty good at it.” The risk-management techniques that Merrill and many other big financial firms had adopted depended heavily on value-at-risk (VAR) models, which dated back to the 1990s, when they were promoted as a means of avoiding a repeat of previous financial blowups, such as the collapse of Barings Bank and the bankruptcy of Orange County. The keys to the appeal of the VAR (or “VaR”) methodology were its simplicity and its apparent precision. By following a fairly straightforward series of steps, the market-risk department of a bank could provide senior management with an exact dollar estimate of the firm’s losses under a worst-case scenario.


pages: 320 words: 33,385

Market Risk Analysis, Quantitative Methods in Finance by Carol Alexander

asset allocation, backtesting, barriers to entry, Brownian motion, capital asset pricing model, constrained optimization, credit crunch, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, en.wikipedia.org, financial engineering, fixed income, implied volatility, interest rate swap, low interest rates, market friction, market microstructure, p-value, performance metric, power law, proprietary trading, quantitative trading / quantitative finance, random walk, risk free rate, risk tolerance, risk-adjusted returns, risk/return, seminal paper, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, stochastic volatility, systematic bias, Thomas Bayes, transaction costs, two and twenty, value at risk, volatility smile, Wiener process, yield curve, zero-sum game

The mean excess loss over a threshold u is defined by eu = EX − u X > u (I.3.67) If the excess over threshold has a generalized Pareto distribution (I.3.65) then the mean excess loss has a simple functional form, eu = β + u 1− (I.3.68) Generalized Pareto distributions have useful applications to value at risk (VaR) measurement. In particular, if the portfolio returns have a GPD distribution there are analytic expressions for the VaR and the expected tail loss (ETL), which is the average of all losses that exceed VaR. Probability and Statistics 105 Expected tail loss (also called conditional VaR) is often used for internal VaR measurement because it is a ‘coherent’ risk measure.21 By definition of the mean excess loss, ETL = VaR + eVaR (I.3.69) So to calculate the ETL from historical loss data we take the losses in excess of the VaR level, estimate the parameters β and of a generalized Pareto distribution and compute the quantity (I.3.68) with u = VaR.

(independent and identically distributed) variables central limit theorem 121 error process 148 financial modelling 186 GEV distribution 101 regression 148, 157, 175 stable distribution 106 stochastic process 134–5 Implicit function 185 Implied volatility 194, 196, 200–1 Implied volatility surface 200–1 Incremental change 31 Indefinite integral 15 Independent events 74 Independent and identically distributed (i.i.d.) variables central limit theorem 121 error process 148 financial modelling 186 GEV distribution 101 regression 148, 157, 175 stable distribution 106 stochastic process 134–5 284 Index Independent variable 72, 143 random 109–10, 115, 140 Index tracking regression model 182–3 Indicator function 6 Indices, laws 8 Indifference curves 248–9 Inequality constraint, minimum variance portfolio 245–6 Inference 72, 118–29, 141 central limit theorem 120–1 confidence intervals 72, 118–24 critical values 118–20, 122–3, 129 hypothesis tests 124–5 means 125–7 non-parametric tests 127–9 quantiles 118–20 variance 126–7 Inflexion points 14, 35 Information matrix 133, 203 Information ratio 257, 259 Instability, finite difference approximation 209–10 Integrated process, discrete time 134–6 Integration 3, 15–16, 35 Intensity, Poisson distribution 88 Interest rate 34, 171–3 Interest rate sensitivity 34 Interpolation 186, 193–200, 223 cubic spline 197–200 currency option 195–7 linear/bilinear 193–5 polynomial 195–7 Intrinsic value of option 215 Inverse function 6–7, 35 Inverse matrix 41, 43–4, 133 Investment bank 225 Investment 2, 256–7 Investor risk tolerance 230–1, 237 Irrational numbers 7 Isoquants 248 Iteration 186–93, 223 bisection method 187–8 gradient method 191–3 Newton–Raphson method 188–91 Itô’s lemma 138–9, 219 iTraxx Europe credit spread index 172 Jacobian matrix 202 Jarque–Bera normality test Jensen’s alpha 257–8 158 Joint density function 114–15 Joint distribution function 114–15 Joint probability 73 Jumps, Poisson process 139 Kappa indices 263–5 Kernel 106–7 Kolmogorov–Smirnoff test 128 Kuhn–Tucker conditions 30 Kurtosis 81–3, 94–6, 205–6 Lagrange multiplier (LM) test 124, 167 Lagrange multiplier 29–30, 244 Lagrangian function 29–30 Lattice 186, 210–16, 223 Laws of indices 8 Least squares OLS estimation 143–4, 146–50, 153–61, 163, 170–1, 176 problems 201–2 weighted 179 Leptokurtic density 82–3 Levenberg–Marquardt algorithm 202 Lévy distribution 105 Likelihood function 72, 130–31 MLE 72, 130–34, 141, 202–3 optimization 202–3 ratio test 124, 167 Linear function 4–5 Linear interpolation 193–5 Linear portfolios 33, 35 correlation matrix 55–60 covariance matrix 55–61 matrix algebra 55–61 P&L 57–8 returns 25, 56–8 volatility 57–8 Linear regression 143–84 Linear restrictions, hypothesis tests 165–6 Linear transformation 48 Linear utility function 233 LM (Lagrange multiplier) 29–30, 124, 167, 244 Local maxima 14, 28–9 Local minima 14, 28–9 Logarithmic utility function 232 Logarithm, natural 1, 9, 34–5 Log likelihood 131–2 Lognormal distribution 93–4, 213–14, 218–20 Log returns 16, 19–25 Index Long portfolio 3, 17, 238–40 Long-short portfolio 17, 20–1 Low discrepancy sequences 217 Lower triangular square matrix 62, 64 LR (likelihood ratio) test 124, 167 LU decomposition, matrix 63–4 Marginal densities 108–9 Marginal distributions 108–9 Marginal probability 73–4 Marginal utility 229–30 Market behaviour 180–1 Market beta 250 Market equilibrium 252 Market maker 2 Market microstructure 180 Market portfolio 250–1 Market risk premium, CAPM 253 Markets complete 212 regime-specific behaviour 96–7 Markowitz, Harry 226, 238, 266 Markowitz problem 200–1, 226, 244–5 Matrix algebra 37–70 application 38–47 decomposition 61–4, 70 definite matrix 37, 46–7, 54, 58–9, 70 determinant 41–3, 47 eigenvalues/vectors 37–8, 48–54, 59–61, 70 functions of several variables 27–31 general linear model 161–2 hypothesis testing 165–6 invariant 62 inverse 41, 43–4 law 39–40 linear portfolio 55–61 OLS estimation 159–61 PCA 64–70 product 39–40 quadratic form 37, 45–6, 54 regression 159–61, 165–6 simultaneous equation 44–5 singular matrix 40–1 terminology 38–9 Maxima 14, 28–31, 35 Maximum likelihood estimation (MLE) 72, 130–4, 141, 202–3 Mean confidence interval 123 Mean excess loss 104 Mean reverting process 136–7 Mean 78–9, 125–6, 127, 133–4 285 Mean square error 201 Mean–variance analysis 238 Mean–variance criterion, utility theory 234–7 Minima 14, 28–31, 35 Minimum variance portfolio 3, 240–7 Mixture distribution 94–7, 116–17, 203–6 MLE (maximum likelihood estimation) 72, 130–4, 141, 202–3 Modified duration 2 Modified Newton method 192–3 Moments probability distribution 78–83, 140 sample 82–3 Sharpe ratio 260–3 Monotonic function 13–14, 35 Monte Carlo simulation 129, 217–22 correlated simulation 220–2 empirical distribution 217–18 random numbers 217 time series of asset prices 218–20 Multicollinearity 170–3, 184 Multiple restrictions, hypothesis testing 166–7 Multivariate distributions 107–18, 140–1 bivariate 108–9, 116–17 bivariate normal mixture 116–17 continuous 114 correlation 111–14 covariance 110–2 independent random variables 109–10, 114 normal 115–17, 220–2 Student t 117–18 Multivariate linear regression 158–75 BHP Billiton Ltd 162–5, 169–70, 174–5 confidence interval 167–70 general linear model 161–2 hypothesis testing 163–6 matrix notation 159–61 multicollinearity 170–3, 184 multiple regression in Excel 163–4 OLS estimation 159–61 orthogonal regression 173–5 prediction 169–70 simple linear model 159–61 Multivariate Taylor expansion 34 Mutually exclusive events 73 Natural logarithm 9, 34–5 Natural spline 198 Negative definite matrix 46–7, 54 Newey–West standard error 176 286 Index Newton–Raphson iteration 188–91 Newton’s method 192 No arbitrage 2, 179–80, 211–12 Non-linear function 1–2 Non-linear hypothesis 167 Non-linear portfolio 33, 35 Non-parametric test 127–9 Normal confidence interval 119–20 Normal distribution 90–2 Jarque–Bera test 158 log likelihood 131–2 mixtures 94–7, 140–1, 203–6 multivariate 115–16, 220–2 standard 218–19 Normalized eigenvector 51–3 Normalized Student t distribution 99 Normal mixture distribution 94–7, 116–17, 140–1 EM algorithm 203–6 kurtosis 95–6 probabilities of variable 96–7 variance 94–6 Null hypothesis 124 Numerical methods 185–223 binomial lattice 210–6 inter/extrapolation 193–200 iteration 186–93 Objective function 29, 188 Offer price 2 Oil index, Amex 162–3, 169–70, 174 OLS (ordinary least squares) estimation 143–4, 146–50 autocorrelation 176 BHP Billiton Ltd case study 163 heteroscedasticity 176 matrix notation 159–61 multicollinearity 170–1 properties of estimator 155–8 regression in Excel 153–5 Omega statistic 263–5 One-sided confidence interval 119–20 Opportunity set 246–7, 251 Optimization 29–31, 200–6, 223 EM algorithm 203–6 least squares problems 201–2 likelihood methods 202–3 numerical methods 200–5 portfolio allocation 3, 181 Options 1–2 American 1, 215–16 Bermudan 1 call 1, 6 currency 195–7 European 1–2, 195–6, 212–13, 215–16 finite difference approximation 206–10 pay-off 6 plain vanilla 2 put 1 Ordinary least squares (OLS) estimation 143–4, 146–50 autocorrelation 176 BHP Billiton Ltd case study 163 heteroscedasticity 176 matrix notation 159–61 multicollinearity 170–1 properties of estimators 155–8 regression in Excel 153–5 Orthogonal matrix 53–4 Orthogonal regression 173–5 Orthogonal vector 39 Orthonormal matrix 53 Orthonormal vector 53 Out-of-sample testing 183 P&L (profit and loss) 3, 19 backtesting 183 continuous time 19 discrete time 19 financial returns 16, 19 volatility 57–8 Pairs trading 183 Parabola 4 Parameter notation 79–80 Pareto distribution 101, 103–5 Parsimonious regression model 153 Partial derivative 27–8, 35 Partial differential equation 2, 208–10 Pay-off, option 6 PCA (principal component analysis) 38, 64–70 definition 65–6 European equity indices 67–9 multicollinearity 171 representation 66–7 Peaks-over-threshold model 103–4 Percentage returns 16, 19–20, 58 Percentile 83–5, 195 Performance measures, RAPMs 256–65 Period log returns 23–5 Pi 7 Index Piecewise polynomial interpolation 197 Plain vanilla option 2 Points of inflexion 14, 35 Poisson distribution 87–9 Poisson process 88, 139 Polynomial interpolation 195–7 Population mean 123 Portfolio allocation 237–49, 266 diversification 238–40 efficient frontier 246–9, 251 Markowitz problem 244–5 minimum variance portfolio 240–7 optimal allocation 3, 181, 247–9 Portfolio holdings 17–18, 25–6 Portfolio mathematics 225–67 asset pricing theory 250–55 portfolio allocation 237–49, 266 RAPMs 256–67 utility theory 226–37, 266 Portfolios bond portfolio 37 delta-hedged 208 linear 25, 33, 35, 55–61 minimum variance 3, 240–7 non-linear 33, 35 rebalancing 17–18, 26, 248–9 returns 17–18, 20–1, 91–2 risk factors 33 risk free 211–12 stock portfolio 37 Portfolio volatility 3 Portfolio weights 3, 17, 25–6 Positive definite matrices 37, 46–7, 70 correlation matrix 58–9 covariance matrix 58–9 eigenvalues/vectors 54 stationary point 28–9 Posterior probability 74 Post-sample prediction 183 Power series expansion 9 Power utility functions 232–3 Prediction 169–70, 183 Price discovery 180 Prices ask price 2 asset price evolution 87 bid price 2 equity 172 generating time series 218–20 lognormal asset prices 213–14 market microstructure 180 offer price 2 stochastic process 137–9 Pricing arbitrage pricing theory 257 asset pricing theory 179–80, 250–55 European option 212–13 no arbitrage 211–13 Principal cofactors, determinants 41 Principal component analysis (PCA) 38, 64–70 definition 65–6 European equity index 67–9 multicollinearity 171 representation 66–7 Principal minors, determinants 41 Principle of portfolio diversification 240 Prior probability 74 Probability and statistics 71–141 basic concepts 72–85 inference 118–29 laws of probability 73–5 MLE 130–4 multivariate distributions 107–18 stochastic processes 134–9 univariate distribution 85–107 Profit and loss (P&L) 3, 19 backtesting 183 continuous time 19 discrete time 19 financial returns 16, 19 volatility 57–8 Prompt futures 194 Pseudo-random numbers 217 Put option 1, 212–13, 215–16 Quadratic convergence 188–9, 192 Quadratic form 37, 45–6, 54 Quadratic function 4–5, 233 Quantiles 83–5, 118–20, 195 Quartiles 83–5 Quasi-random numbers 217 Random numbers 89, 217 Random variables 71 density/distribution function 75 i.i.d. 101, 106, 121, 135, 148, 157, 175 independent 109–10, 116, 140–1 OLS estimators 155 sampling 79–80 Random walks 134–7 Ranking investments 256 287 288 Index RAPMs (risk adjusted performance measures) 256–67 CAPM 257–8 kappa indices 263–5 omega statistic 263–5 Sharpe ratio 250–1, 252, 257–63, 267 Sortino ratio 263–5 Realization, random variable 75 Realized variance 182 Rebalancing of portfolio 17–18, 26, 248–9 Recombining tree 210 Regime-specific market behaviour 96–7, 117 Regression 143–84 autocorrelation 175–9, 184 financial applications 179–83 heteroscedasticity 175–9, 184 linear 143–84 multivariate linear 158–75 OLS estimator properties 155–8 simple linear model 144–55 Relative frequency 77–8 Relative risk tolerance 231 Representation, PCA 66–7 Residuals 145–6, 157, 175–8 Residual sum of squares (RSS) 146, 148–50, 159–62 Resolution techniques 185–6 Restrictions, hypothesis testing 165–7 Returns 2–3, 16–26 absolute 58 active 92, 256 CAPM 253–4 compounding 22–3 continuous time 16–17 correlated simulations 220 discrete time 16–17, 22–5 equity index 96–7 geometric Brownian motion 21–2 linear portfolio 25, 56–8 log returns 16, 19–25 long-short portfolio 20–1 multivariate normal distribution 115–16 normal probability 91–2 P&L 19 percentage 16, 19–20, 59–61 period log 23–5 portfolio holdings/weights 17–18 risk free 2 sources 25–6 stochastic process 137–9 Ridge estimator, OLS 171 Risk active risk 256 diversifiable risk 181 portfolio 56–7 systematic risk 181, 250, 252 Risk adjusted performance measure (RAPM) 256–67 CAPM 257–8, 266 kappa indices 263–5 omega statistic 263–5 Sharpe ratio 251, 252, 257–63, 267 Sortino ratio 263–5 Risk averse investor 248 Risk aversion coefficients 231–4, 237 Risk factor sensitivities 33 Risk free investment 2 Risk free portfolio 211 Risk free returns 2 Risk loving investors 248–9 Risk neutral valuation 211–12 Risk preference 229–30 Risk reversal 195–7 Risk tolerance 230–1, 237 Robustness 171 Roots 3–9, 187 RSS (residual sum of squares) 146, 148–50, 159–62 S&P 100 index 242–4 S&P 500 index 204–5 Saddle point 14, 28 Sample 76–8, 82–3 Sampling distribution 140 Sampling random variable 79–80 Scalar product 39 Scaling law 106 Scatter plot 112–13, 144–5 SDE (stochastic differential equation) 136 Security market line (SML) 253–4 Self-financing portfolio 18 Sensitivities 1–2, 33–4 Sharpe ratio 257–63, 267 autocorrelation adjusted 259–62 CML 251, 252 generalized 262–3 higher moment adjusted 260–2 making decision 258 stochastic dominance 258–9 Sharpe, William 250 Short portfolio 3, 17 22, 134, Index Short sales 245–7 Short-term hedging 182 Significance level 124 Similarity transform 62 Similar matrices 62 Simple linear regression 144–55 ANOVA and goodness of fit 149–50 error process 148–9 Excel OLS estimation 153–5 hypothesis tests 151–2 matrix notation 159–61 OLS estimation 146–50 reporting estimated model 152–3 Simulation 186, 217–22 Simultaneous equations 44–5 Singular matrix 40–1 Skewness 81–3, 205–6 Smile fitting 196–7 SML (security market line) 253–4 Solver, Excel 186, 190–1, 246 Sortino ratio 263–5 Spectral decomposition 60–1, 70 Spline interpolation 197–200 Square matrix 38, 40–2, 61–4 Square-root-of-time scaling rule 106 Stable distribution 105–6 Standard deviation 80, 121 Standard error 80, 169 central limit theorem 121 mean/variance 133–4 regression 148–9 White’s robust 176 Standard error of the prediction 169 Standardized Student t distribution 99–100 Standard normal distribution 90, 218–19 Standard normal transformation 90 Standard uniform distribution 89 Stationary point 14–15, 28–31, 35 Stationary stochastic process 111–12, 134–6 Stationary time series 64–5 Statistical arbitrage strategy 182–3 Statistical bootstrap 218 Statistics and probability 71–141 basic concepts 72–85 inference 118–29 law of probability 73–5 MLE 130–4 multivariate distribution 107–18 stochastic process 134–9 univariate distribution 85–107 Step length 192 Stochastic differential equation (SDE) 22, 134, 136 Stochastic dominance 227, 258–9 Stochastic process 72, 134–9, 141 asset price/returns 137–9 integrated 134–6 mean reverting 136–7 Poisson process 139 random walks 136–7 stationary 111–12, 134–6 Stock portfolio 37 Straddle 195–6 Strangle 195–7 Strictly monotonic function 13–14, 35 Strict stochastic dominance 258 Structural break 175 Student t distribution 97–100, 140 confidence intervals 122–3 critical values 122–3 equality of means/variances 127 MLE 132 multivariate 117–18 regression 151–3, 165, 167–8 simulation 220–2 Sum of squared residual, OLS 146 Symmetric matrix 38, 47, 52–4, 61 Systematic risk 181, 250, 252 Tail index 102, 104 Taylor expansion 2–3, 31–4, 36 applications 33–4 approximation 31–4, 36 definition 32–3 multivariate 34 risk factor sensitivities 33 Theory of asset pricing 179–80, 250–55 Tic-by-tic data 180 Time series asset prices/returns 137–9, 218–20 lognormal asset prices 218–20 PCA 64–5 Poisson process 88 regression 144 stochastic process 134–9 Tobin’s separation theorem 250 Tolerance levels, iteration 188 Tolerance of risk 230–1, 237 Total derivative 31 Total sum of square (TSS) 149, 159–62 289 290 Index Total variation, PCA 66 Tower law for expectations 79 Traces of matrix 62 Tradable asset 1 Trading, regression model 182–3 Transition probability 211–13 Transitive preferences 226 Transposes of matrix 38 Trees 186, 209–11 Treynor ratio 257, 259 TSS (total sum of squares) 149, 159–62 Two-sided confidence interval 119–21 Unbiased estimation 79, 81, 156–7 Uncertainty 71 Unconstrained optimization 29 Undiversifiable risk 252 Uniform distribution 89 Unit matrix 40–1 Unit vector 46 Univariate distribution 85–107, 140 binomial 85–7, 212–13 exponential 87–9 generalized Pareto 101, 103–5 GEV 101–3 kernel 106–7 lognormal 93–4, 213–14, 218–20 normal 90–7, 115–16, 131–2, 140, 157–8, 203–6, 217–22 normal mixture 94–7, 140, 203–6 Poisson 87–9 sampling 100–1 stable 105–6 Student t 97–100, 122–3, 126, 132–3, 140–1, 151–3, 165–8, 220–2 uniform 89 Upper triangular square matrix 62, 64 Utility theory 226–37, 266 mean–variance criterion 234–7 properties 226–9 risk aversion coefficient 231–4, 237 risk preference 229–30 risk tolerance 230–1, 237 Value at risk (VaR) 104–6, 185, 194 Vanna–volga interpolation method 196 Variance ANOVA 143–4, 149–50, 154, 159–60, 164–5 confidence interval 123–4 forecasting 182 minimum variance portfolio 3, 240–7 mixture distribution 94–6 MLE 133 normal mixture distribution 95–6 portfolio volatility 3 probability distribution 79–81 realized 182 tests on variance 126–7 utility theory 234–7 VaR (value at risk) 104–6, 185, 194 Vector notation, functions of several variables 28 Vectors 28, 37–9, 48–54, 59–61, 70 Venn diagram 74–5 Volatility equity 3, 172–3 implied volatility 194, 196–7, 200–1 interpolation 194, 196–7 linear portfolio 57–8 long-only portfolio 238–40 minimum variance portfolio 240–4 portfolio variance 3 Volpi, Leonardo 70 Vstoxx index 172 Waiting time, Poisson process 88–9 Wald test 124, 167 Weakly stationary process 135 Weak stochastic dominance 258–9 Weibull distribution 103 Weighted least squares 179 Weights, portfolio 3, 17, 25–6 White’s heteroscedasticity test 177–8 White’s robust standard errors 176 Wiener process 22, 136 Yield 1, 197–200 Zero matrix 39 Z test 126

The allocations to risky assets that give portfolios with the minimum possible risk (as measured by the portfolio volatility) can only be determined analytically when there are no specific constraints on the allocations such as ‘no more than 5% of the capital should be allocated to US bonds’. The value at risk (VaR) of a portfolio has an analytic solution only under certain assumptions about the portfolio and its returns process. Otherwise we need to use a numerical method – usually simulation – to compute the VaR of a portfolio. The yield on a bond is the constant discount rate that, when applied to the future cash flows from the bond, gives its market price. Given the market price of a typical bond, we can only compute its yield using a numerical method.


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I.O.U.: Why Everyone Owes Everyone and No One Can Pay by John Lanchester

Alan Greenspan, asset-backed security, bank run, banking crisis, Bear Stearns, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black Monday: stock market crash in 1987, Black-Scholes formula, Blythe Masters, Celtic Tiger, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, Daniel Kahneman / Amos Tversky, diversified portfolio, double entry bookkeeping, Exxon Valdez, Fall of the Berlin Wall, financial deregulation, financial engineering, financial innovation, fixed income, George Akerlof, Glass-Steagall Act, greed is good, Greenspan put, hedonic treadmill, hindsight bias, housing crisis, Hyman Minsky, intangible asset, interest rate swap, invisible hand, James Carville said: "I would like to be reincarnated as the bond market. You can intimidate everybody.", Jane Jacobs, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, junk bonds, Kickstarter, laissez-faire capitalism, light touch regulation, liquidity trap, Long Term Capital Management, loss aversion, low interest rates, Martin Wolf, money market fund, mortgage debt, mortgage tax deduction, mutually assured destruction, Myron Scholes, negative equity, new economy, Nick Leeson, Norman Mailer, Northern Rock, off-the-grid, Own Your Own Home, Ponzi scheme, quantitative easing, reserve currency, Right to Buy, risk-adjusted returns, Robert Shiller, Ronald Reagan, Savings and loan crisis, shareholder value, South Sea Bubble, statistical model, Tax Reform Act of 1986, The Great Moderation, the payments system, too big to fail, tulip mania, Tyler Cowen, value at risk

RAROC offered a numerical analysis of risk and added to it a measure of the impact of that risk on a business’s profitability; just as portfolio management provided a way of assessing and optimizing the risk of a set of share holdings, RAROC did the same for a company’s or bank’s range of businesses. In time, however, the industry came to prefer a newer model of risk, called value at risk, or VAR. This was a statistical technique which really took off in the later 1980s, as a response to the Black Monday stock market crash of October 1987. On that occasion, many players were appalled by the speed and severity of their losses—losses which, it’s now thought, were in large part caused by computer programs running “portfolio insurance.”

., 43, 54, 64, 74, 76–78 AIG bailout and, 76, 78 regulation and, 188–90 Treasury bills (T-bills), 29–30, 62, 103, 118, 144, 208 China’s investment in, 109, 176–77 Trichet, Jean-Claude, 92 Trillion Dollar Meltdown, The (Morris), 42 Troubled Assets Relief Program (TARP), 37, 189 Turner, Adair, 181 Tversky, Amos, 136–38, 141 UBS, 36, 120 uncertainty, 96 fair value theory and, 147–48 risk and, 55–56, 153, 163 United Kingdom, 9, 11–12, 18, 28–29, 61, 122–24, 134, 139, 194–202, 216–18 banking in, 5, 11, 32–36, 38–40, 51–54, 76–77, 89, 94, 120, 146, 180, 194–96, 199, 202, 204–6, 211–12, 217, 227–28 bill of, 220–22, 224 and City of London, 21–22, 32, 195–97, 200, 217–18 credit ratings and, 123–24, 209 derivatives and, 72, 200–201 financial vs. industrial interests in, 196–99 free-market capitalism in, 14–15, 21, 230 GDP of, 32, 214, 220 Goodwin’s pension and, 76–77 housing in, 38, 87–98, 110, 122, 177–78 interest rates in, 102, 177–80 personal debt in, 221–22 prosperity of, 214, 216 regulation in, 21–22, 105n, 180–82, 194–96, 199–201, 218 United Nations, 4 United States, 17–22, 34, 62–71, 120–31, 134n, 165, 199–201 AIG bailout and, 76–78 banks of, 36–37, 39–40, 43, 63–71, 73, 75, 77–78, 84, 116, 120–21, 127, 150, 152, 163, 183, 185, 190, 195, 204, 211–12, 219–20, 225, 227–28 bill of, 219–20 China’s investment in, 109, 176–77 credit and, 109, 123–24, 195, 208–9, 211 free-market capitalism in, 14–15, 230 housing in, 37, 82–86, 95, 97–101, 109–10, 114–15, 122, 125–31, 157–58, 163 interest rates in, 102, 107–8, 173–77 regulation in, 181, 184–92, 195, 199–200, 223–24, 227 urban desolation in, 81–86 value, values, 42, 74–75, 78–80, 103–4, 179, 181, 217–18, 220, 227 bonds and, 61, 103 derivatives and, 38, 48–49, 185, 201 housing and, 28–29, 71, 90, 92–95, 111, 176 investing and, 60–61, 104, 198 LTCM and, 55–56 notional, 38, 48–49, 80 value at risk (VAR), 151–57, 162–63 Vietnam War, 18, 220 Viniar, David, 163 volatility, 20, 158 risk and, 47–48, 148–50, 161 Volcker, Paul, 20 Waldrow, Mary, 127 Wall Street, 22, 53, 64, 129, 188 Washington Post, The, 18 wealth, 4, 10, 19–21, 64, 204, 206 financial industry’s ascent and, 20–21 in free-market capitalism, 15, 19, 230 housing and, 87, 90, 121 Keynes’s predictions on, 214–15 in West, 218–19 Weatherstone, Dennis, 152 Wells Fargo, 84, 127 Wessex Water, 105n West, 14–18, 43, 213, 231 conflict between Communist bloc and, 16–18 free-market capitalism in, 14–15, 17, 21, 23 wealth in, 218–19 wheat, 49n, 52 When Genius Failed (Lowenstein), 161 Williams, John Burr, 147 Wilson, Lashawn, 130–31 Wire, The, 83–84 World Bank, 58, 65, 69 * GDP, which will be mentioned quite a few times in this story, sounds complicated but isn’t: it’s nothing more than the value of all the goods and services produced in an economy.

In spite of naysayers, VAR is an essential component of sound risk management systems. VAR gives an estimate of potential losses given market risks. In the end, the greatest benefit of VAR lies in the imposition of a structured methodology for critically thinking about risk. Institutions that go through the process of computing their VAR are forced to confront their exposure to financial risks and to set up a proper risk management function. Thus the process of getting to VAR may be as important as the number itself. So what VAR boils down to is that it forces people to think properly about risk.


pages: 701 words: 199,010

The Crisis of Crowding: Quant Copycats, Ugly Models, and the New Crash Normal by Ludwig B. Chincarini

affirmative action, Alan Greenspan, asset-backed security, automated trading system, bank run, banking crisis, Basel III, Bear Stearns, Bernie Madoff, Black-Scholes formula, Bob Litterman, business cycle, buttonwood tree, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, delta neutral, discounted cash flows, diversification, diversified portfolio, family office, financial engineering, financial innovation, financial intermediation, fixed income, Flash crash, full employment, Gini coefficient, Glass-Steagall Act, global macro, high net worth, hindsight bias, housing crisis, implied volatility, income inequality, interest rate derivative, interest rate swap, John Meriwether, Kickstarter, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low interest rates, low skilled workers, managed futures, margin call, market design, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, Mitch Kapor, money market fund, moral hazard, mortgage debt, Myron Scholes, National best bid and offer, negative equity, Northern Rock, Occupy movement, oil shock, price stability, proprietary trading, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Waldo Emerson, regulatory arbitrage, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, Robert Shiller, Ronald Reagan, Sam Peltzman, Savings and loan crisis, Sharpe ratio, short selling, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, survivorship bias, systematic trading, tail risk, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond

Good models could generate more complicated security-price distributions, but might also ultimately measure risks more accurately.3 Risk models’ failure to account for crowding and interconnectedness also played an important role in risk misvaluation during 2008’s financial crisis.4 VaR There are limitations to VaR, stress testing, or any other risk management system. LTCM’s risk management system rested on selecting a portfolio with a large number of low-volatility trades, all with very low correlations to one another. The portfolio had a very low estimated value-at-risk (VaR) before the fund collapsed in August and September 1998. Extremely large directional bets often bring down traders and hedge funds, but this wasn’t the case for LTCM. LTCM’s failure illustrates some of the limitations of VaR analysis and stress testing—as well as the impossibility of stress testing the unimaginable.

If traders measured this correlation inaccurately and correlations across strategies were in fact higher than estimated, the fund’s loss risk was much larger. A simple value-at-risk (VaR) formula for the above structure is: (A.9) where represents the expected return of the levered portfolio, represents the standard deviation of the levered portfolio, Vt represents the initial portfolio value, and k represents the confidence level critical value, assuming a normal distribution (i.e., k = 1.96 for a 97.5% confidence interval).9 Table A.1 presents the potential VaR calculations at a 99% confidence level for a normal distribution (k = 2.33) and a capital base of $4.8B (the amount that LTCM had at the beginning of 1998). The VaR numbers are presented as monthly numbers.

Portfolio returns come from a return distribution. That distribution may include a –100% return, which means losing the entire portfolio. Thus, one way to measure risk is to measure the worst-case scenario: losing everything. That doesn’t tell us very much about more typical risks. A more useful risk measurement uses a portfolio’s value-at-risk (VaR). This measure gives an estimate of the largest losses a portfolio is likely to suffer in a given period in all but truly exceptional circumstances. The calculation depends on a host of inputs, including the portfolio’s expected return, the portfolio’s volatility, and the fund manager’s degree of confidence in the largest loss.


pages: 543 words: 157,991

All the Devils Are Here by Bethany McLean

Alan Greenspan, Asian financial crisis, asset-backed security, bank run, Bear Stearns, behavioural economics, Black-Scholes formula, Blythe Masters, break the buck, buy and hold, call centre, Carl Icahn, collateralized debt obligation, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, diversification, Dr. Strangelove, Exxon Valdez, fear of failure, financial innovation, fixed income, Glass-Steagall Act, high net worth, Home mortgage interest deduction, interest rate swap, junk bonds, Ken Thompson, laissez-faire capitalism, Long Term Capital Management, low interest rates, margin call, market bubble, market fundamentalism, Maui Hawaii, Michael Milken, money market fund, moral hazard, mortgage debt, Northern Rock, Own Your Own Home, Ponzi scheme, proprietary trading, quantitative trading / quantitative finance, race to the bottom, risk/return, Ronald Reagan, Rosa Parks, Savings and loan crisis, shareholder value, short selling, South Sea Bubble, statistical model, stock buybacks, tail risk, Tax Reform Act of 1986, telemarketer, the long tail, too big to fail, value at risk, zero-sum game

Its breakthrough risk model was called Value at Risk, or VaR. Both products quickly became tools that everyone on Wall Street relied on. What did these innovations have to do with subprime mortgages? Nothing, at first. J.P. Morgan and Ameriquest could have been operating on different planets, so little did they have to do with each other. But in time, Wall Street realized that the same principles that underlay J.P. Morgan’s risk model could be adapted to bestow coveted triple-A ratings on large chunks of complex new products created out of subprime mortgages. Firms could use VaR to persuade regulators—and themselves—that they were taking on very little risk, even as they were loading up on subprime securities.

Later named as a defendant in the SEC’s suit against the company. David Viniar CFO. J.P. Morgan Mark Brickell Lobbyist who fought derivatives regulation on behalf of J.P. Morgan and the International Swaps and Derivatives Association. President of ISDA from 1988 to 1992. Till Guldimann Executive who led the development of Value at Risk modeling and shared VaR with other banks. Blythe Masters Derivatives saleswoman who put together J.P. Morgan’s first credit default swap in 1994. Sir Dennis Weatherstone Chairman and CEO from 1990 to 1994. Merrill Lynch Michael Blum Executive charged with purchasing a mortgage company, First Franklin, in 2006.

The first of two laws passed in the 1980s to aid the new mortgage-backed securities market SIV: Structured investment vehicle. Thinly capitalized entities set up by banks and others to invest in securities. By the height of the boom, many ended up owning billions in CDOs and other mortgage-backed securities. VaR: Value at Risk. Key measure of risk developed by J.P. Morgan in the early 1990s. Prologue Stan O’Neal wanted to see him. How strange. It was September 2007. The two men hadn’t talked in years, certainly not since O’Neal had become CEO of Merrill Lynch in 2002. Back then, John Breit had been one of the company’s most powerful risk managers.


pages: 923 words: 163,556

Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization: The Ideal Risk, Uncertainty, and Performance Measures by Frank J. Fabozzi

algorithmic trading, Benoit Mandelbrot, Black Monday: stock market crash in 1987, capital asset pricing model, collateralized debt obligation, correlation coefficient, distributed generation, diversified portfolio, financial engineering, fixed income, global macro, index fund, junk bonds, Louis Bachelier, Myron Scholes, p-value, power law, quantitative trading / quantitative finance, random walk, risk free rate, risk-adjusted returns, short selling, stochastic volatility, subprime mortgage crisis, Thomas Bayes, transaction costs, value at risk

At q0.2 = 2, this area is exactly equal to 0.2. Value-at-Risk Let’s look at how quantiles are related to an important risk measure used by financial institutions called value-at-risk (VaR). Consider a portfolio consisting of financial assets. Suppose the return of this portfolio is given by rP. Denoting today’s portfolio value by P0, the value of the portfolio tomorrow is assumed to followFIGURE 13.2 Determining the 0.2-Quantile using the Cumulative Distribution Function P1 = P0 ⋅ e rP As is the case for quantiles, in general, VaR is associated with some level α Then, VaRα states that with probability 1 - α, the portfolio manager incurs a loss of VaRα or more.

That is, for X defined on the probability space (Ω, A, P), the conditional variance is a random variable measurable with respect to the sub-σ-algebra, G, and denoted as var[X|G] such that its variance on each set of G is equal to the variance of X on the same set. For example, with the question “During recessionary periods, what is the variability in the number of corporate loans that default?,” we are looking to computevar[Number of default corporate loans | State of the economy = Recession] using the definition of variance from Chapter 13, we could equivalently express var[X|G] in terms of conditional expectations,var[X|G] = E[X2|G] + E[X|G]2 Expected Tail Loss In Chapter 13, we explained that the value-at-risk (VaR) is one risk measure employed in financial risk measurement and management.

To answer this question, we must compute the expectation of losses conditional on the 99% VaR being exceeded:E[-Rt |-Rt > 99% VaR] where Rt denotes the return on a financial asset at time t and -Rt—the loss at time t. In the general case of VaR(1-α)100%, the conditional expectation above takes the formE[-Rt | -Rt > VaR(1-α)100%] and is known as the (1 - α)100% expected tail loss (ETL) or conditional value-at-risk (CVaR) if the return Rt has density. CONCEPTS EXPLAINED IN THIS CHAPTER (IN ORDER OF PRESENTATION) Conditional probabilities unconditional probabilities Marginal probabilities Independent events Dependent events Joint probability Multiplicative Rule of Probability Law of Total Probability Bayes’ rule Conditional parameters Conditional expectations Conditional variance Expected tail loss Conditional value-at-risk CHAPTER 16 Copula and Dependence Measures In previous chapters of this book, we introduced multivariate distributions that had distribution functions that could be presented as functions of their parameters and the values x of the state space; in other words, they could be given in closed form.175 In particular, we learned about the multivariate normal and multivariate t-distributions.


How I Became a Quant: Insights From 25 of Wall Street's Elite by Richard R. Lindsey, Barry Schachter

Albert Einstein, algorithmic trading, Andrew Wiles, Antoine Gombaud: Chevalier de Méré, asset allocation, asset-backed security, backtesting, bank run, banking crisis, Bear Stearns, Black-Scholes formula, Bob Litterman, Bonfire of the Vanities, book value, Bretton Woods, Brownian motion, business cycle, business process, butter production in bangladesh, buy and hold, buy low sell high, capital asset pricing model, centre right, collateralized debt obligation, commoditize, computerized markets, corporate governance, correlation coefficient, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency risk, discounted cash flows, disintermediation, diversification, Donald Knuth, Edward Thorp, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, financial engineering, financial innovation, fixed income, full employment, George Akerlof, global macro, Gordon Gekko, hiring and firing, implied volatility, index fund, interest rate derivative, interest rate swap, Ivan Sutherland, John Bogle, John von Neumann, junk bonds, linear programming, Loma Prieta earthquake, Long Term Capital Management, machine readable, margin call, market friction, market microstructure, martingale, merger arbitrage, Michael Milken, Myron Scholes, Nick Leeson, P = NP, pattern recognition, Paul Samuelson, pensions crisis, performance metric, prediction markets, profit maximization, proprietary trading, purchasing power parity, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Reminiscences of a Stock Operator, Richard Feynman, Richard Stallman, risk free rate, risk-adjusted returns, risk/return, seminal paper, shareholder value, Sharpe ratio, short selling, Silicon Valley, six sigma, sorting algorithm, statistical arbitrage, statistical model, stem cell, Steven Levy, stochastic process, subscription business, systematic trading, technology bubble, The Great Moderation, the scientific method, too big to fail, trade route, transaction costs, transfer pricing, value at risk, volatility smile, Wiener process, yield curve, young professional

This goes to show that sometimes the business model is more important than the quantitative model. JWPR007-Lindsey May 7, 2007 17:9 Julian Shaw 235 The Strange Evolution of Value at Risk In the beginning, VaR calculations were usually based on the assumption of a multivariate normal distribution of a large number of market risk factors, a method generally known as variance-covariance or VCV. The variances and covariances of these factors were estimated with complex GARCH models (or particularizations of GARCH such as exponential smoothing). What is the situation today? Are today’s VaR models even more complex? No, they are much simpler! Almost everyone, even at JP Morgan where the multivariate normal approach was invented, has switched to an approach so simple even my mother can understand—historical simulation, or HistSim.

., 171 Summer experience, impact, 57 Sun Unix workstation, 22 Surplus insurance, usage, 255–256 Swaps rate, Black volatilities, 172 usage, 292–293 Sweeney, Richard, 190 Symbolics, 16, 18 Taleb, Nassim, 132 Tenenbein, Aaron, 252 Textbook learning, expansion, 144 Theoretical biases, 103 Theory, usage/improvement, 182–185 Thornton, Dan, 139 Time diversification, myths, 258 Top secret, classification, 16–18 Tracking error, focus, 80–81 Trading, 72–73 Transaction cost, 129 absence, 247 impact, 273–274 Transaction pricing, decision-making process, 248 Transistor experiment (TX), 11 Transistorized Experimental Computer Zero (tixo), usage, 86 Treynor, Jack, 34, 254 Trigger, usage, 117–118 Trimability, 281 TRS-80 (Radio Shack), usage, 50, 52, 113 Trust companies, individually managed accounts (growth), 79 Tucker, Alan, 334 Uncertainty examination, 149–150 resolution, 323–324 Unit initialization, 172 Universal Investment Reasoning, 19–20 Upstream Technologies, LLC, 67 U.S. individual stock data, research, 201–202 Value-at-Risk (VaR), 195. calculation possibility tails, changes, 100 design, 293 evolution, 235 measurement, 196 number, emergence, 235 van Eyseren, Olivier, 173–175 Vanilla interest swaptions, 172 VarianceCoVariance (VCV), 235 Variance reduction techniques, 174 Vector auto-regression (VAR), 188 Venture capital investments, call options (analogy), 145–146 Volatility, 100, 174, 193–194 Volcker, Paul, 32 von Neumann, John, 319 Waddill, Marcellus, 318 Wall Street business, arrival, 61–65 interest, 160–162 move, shift, 125–127 quant search, genesis, 32 roots, 83–85 Wanless, Derek, 173 Wavelets, 239 Weisman, Andrew B., 187–196 Wells Fargo Nikko Investment Advisors, Grinold (attachment), 44 Westlaw database, 146–148 “What Practitioners Need to Know” (Kritzman), 255 Wigner, Eugene, 54 Wiles, Andrew, 112 Wilson, Thomas C., 95–105 Windham Capital Management LLC, 251, 254 Wires, rat consumption (prevention), 20–23 Within-horizon risk, usage, 256 Worker longevity, increase, 148 Wyckoff, Richard D., 321 Wyle, Steven, 18 Yield, defining, 182 Yield curve, 89–90, 174 Zimmer, Bob, 131–132

For brevity’s sake, I have excluded some themes, including my contributions into asset/liability management for both banks and insurance companies. JWPR007-Lindsey May 7, 2007 16:50 Thomas C. Wilson 99 The Early 1990s: The Market Risk Era The early 1990s marked the market risk era. During this period, the banking industry developed economic capital or value-at-risk (VaR) models, Raroc performance measures,1 and treasury funds transfer pricing rules in order to answer questions with substantial strategic consequences. In order to understand these developments, as well as the contributions I made during this period, it is useful to provide some historical context.


pages: 297 words: 91,141

Market Sense and Nonsense by Jack D. Schwager

3Com Palm IPO, asset allocation, Bear Stearns, Bernie Madoff, Black Monday: stock market crash in 1987, Brownian motion, buy and hold, collateralized debt obligation, commodity trading advisor, computerized trading, conceptual framework, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, fixed income, global macro, high net worth, implied volatility, index arbitrage, index fund, Jim Simons, junk bonds, London Interbank Offered Rate, Long Term Capital Management, low interest rates, managed futures, margin call, market bubble, market fundamentalism, Market Wizards by Jack D. Schwager, merger arbitrage, negative equity, pattern recognition, performance metric, pets.com, Ponzi scheme, proprietary trading, quantitative trading / quantitative finance, random walk, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, selection bias, Sharpe ratio, short selling, statistical arbitrage, statistical model, subprime mortgage crisis, survivorship bias, tail risk, transaction costs, two-sided market, value at risk, yield curve

It is not a sufficient indicator because in some cases, high volatility may be due to large gains while losses are well controlled. The Problem with Value at Risk (VaR) Value at risk (VaR) is a worst-case loss estimate that is most prone to serious error in worst-case situations. The VaR can be defined as the loss threshold that will not be exceeded within a specified time interval at some high confidence level (typically, 95 percent or 99 percent). The VaR can be stated in either dollar or percentage terms. For example, a 3.2 percent daily VaR at the 99 percent confidence level would imply that the daily loss is expected to exceed 3.2 percent on only 1 out of 100 days. To convert a VaR from daily to monthly, we multiply it by 4.69, the square root of 22 (the approximate number of trading days in a month).

Reality: For portfolios with significant illiquid holdings, the value that would be realized if the portfolio had to be liquidated might be considerably lower than implied by market prices because of the slippage that would occur in exiting positions. Investment Misconception 14: Value at risk (VaR) provides a good indication of worst-case risk. Reality: VaR may severely understate worst-case risk when the look-back period used to calculate this statistic is not representative of the future volatility and correlation levels of the portfolio holdings. Following transitions from benign market environments to liquidation-type markets, realized losses can far exceed the thresholds implied by previous VaR levels. By the time VaR adequately adjusts to the new high-risk environment, larger-than-anticipated losses may already have been realized.

Hedge Funds: Relative Performance of the Past Highest-Return Strategy Why Do Past High-Return Sectors and Strategy Styles Perform So Poorly? Wait a Minute. Do We Mean to Imply . . .? Investment Insights Chapter 4: The Mismeasurement of Risk Worse Than Nothing Volatility as a Risk Measure The Source of the Problem Hidden Risk Evaluating Hidden Risk The Confusion between Volatility and Risk The Problem with Value at Risk (VaR) Asset Risk: Why Appearances May Be Deceiving, or Price Matters Investment Insights Chapter 5: Why Volatility Is Not Just about Risk, and the Case of Leveraged ETFs Leveraged ETFs: What You Get May Not Be What You Expect Investment Insights Chapter 6: Track Record Pitfalls Hidden Risk The Data Relevance Pitfall When Good Past Performance Is Bad The Apples-and-Oranges Pitfall Longer Track Records Could Be Less Relevant Investment Insights Chapter 7: Sense and Nonsense about Pro Forma Statistics Investment Insights Chapter 8: How to Evaluate Past Performance Why Return Alone Is Meaningless Risk-Adjusted Return Measures Visual Performance Evaluation Investment Insights Chapter 9: Correlation: Facts and Fallacies Correlation Defined Correlation Shows Linear Relationships The Coefficient of Determination (r2) Spurious (Nonsense) Correlations Misconceptions about Correlation Focusing on the Down Months Correlation versus Beta Investment Insights Part Two: Hedge Funds as an Investment Chapter 10: The Origin of Hedge Funds Chapter 11: Hedge Funds 101 Differences between Hedge Funds and Mutual Funds Types of Hedge Funds Correlation with Equities Chapter 12: Hedge Fund Investing: Perception and Reality The Rationale for Hedge Fund Investment Advantages of Incorporating Hedge Funds in a Portfolio The Special Case of Managed Futures Single-Fund Risk Investment Insights Chapter 13: Fear of Hedge Funds: It’s Only Human A Parable Fear of Hedge Funds Chapter 14: The Paradox of Hedge Fund of Funds Underperformance Investment Insights Chapter 15: The Leverage Fallacy The Folly of Arbitrary Investment Rules Leverage and Investor Preference When Leverage Is Dangerous Investment Insights Chapter 16: Managed Accounts: An Investor-Friendly Alternative to Funds The Essential Difference between Managed Accounts and Funds The Major Advantages of a Managed Account Individual Managed Accounts versus Indirect Managed Account Investment Why Would Managers Agree to Managed Accounts?


pages: 345 words: 86,394

Frequently Asked Questions in Quantitative Finance by Paul Wilmott

Abraham Wald, Albert Einstein, asset allocation, beat the dealer, Black-Scholes formula, Brownian motion, butterfly effect, buy and hold, capital asset pricing model, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, delta neutral, discrete time, diversified portfolio, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial engineering, fixed income, fudge factor, implied volatility, incomplete markets, interest rate derivative, interest rate swap, iterative process, lateral thinking, London Interbank Offered Rate, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, market bubble, martingale, Myron Scholes, Norbert Wiener, Paul Samuelson, power law, quantitative trading / quantitative finance, random walk, regulatory arbitrage, risk free rate, risk/return, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, stochastic volatility, transaction costs, urban planning, value at risk, volatility arbitrage, volatility smile, Wiener process, yield curve, zero-coupon bond

Boston and New York: Houghton Mifflin Wilmott, P 2006 Paul Wilmott On Quantitative Finance, second edition. John Wiley & Sons What is Value at Risk and How is it Used? Short Answer Value at Risk, or VaR for short, is a measure of the amount that could be lost from a position, portfolio, desk, bank, etc. VaR is generally understood to mean the maximum loss an investment could incur at a given confidence level over a specified time horizon. There are other risk measures used in practice but this is the simplest and most common. Example An equity derivatives hedge fund estimates that its Value at Risk over one day at the 95% confidence level is $500,000. This is interpreted as one day out of 20 the fund expects to lose more than half a million dollars.

Structured products Contracts designed to meet the specific investment criteria of a client, in terms of market view, risk and return. Swap A general term for an over-the-counter contract in which there are exchanges of cashflows between two parties. See page 324. Swaptions An option on a swap. They are commonly Bermudan exercise. See page 324. VaR Value at Risk, an estimate of the potential downside from one’s investments. See pages 40 and 48. Variance swap A contract in which there is an exchange of the realized variance over a specified period and a fixed amount. See page 325. Volatility The annualized standard deviation of returns of an asset.

• It does not tell you what the loss will be beyond the VaR value • VaR is concerned with typical market conditions, not the extreme events • It uses historical data, “like driving a car by looking in the rear-view mirror only” • Within the time horizon positions could change dramatically (due to normal trading or due to hedging or expiration of derivatives) A common criticism of traditional VaR has been that it does not satisfy all of certain commonsense criteria. Artzner et al. (1997) specify criteria that make a risk measure coherent. And VaR as described above is not coherent. Prudence would suggest that other risk-measurement methods are used in conjunction with VaR, including but not limited to, stress testing under different real and hypothetical scenarios, including the stressing of volatility especially for portfolios containing derivatives.


pages: 317 words: 106,130

The New Science of Asset Allocation: Risk Management in a Multi-Asset World by Thomas Schneeweis, Garry B. Crowder, Hossein Kazemi

asset allocation, backtesting, Bear Stearns, behavioural economics, Bernie Madoff, Black Swan, book value, business cycle, buy and hold, capital asset pricing model, collateralized debt obligation, commodity trading advisor, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, diversification, diversified portfolio, financial engineering, fixed income, global macro, high net worth, implied volatility, index fund, interest rate swap, invisible hand, managed futures, market microstructure, merger arbitrage, moral hazard, Myron Scholes, passive investing, Richard Feynman, Richard Feynman: Challenger O-ring, risk free rate, risk tolerance, risk-adjusted returns, risk/return, search costs, selection bias, Sharpe ratio, short selling, statistical model, stocks for the long run, survivorship bias, systematic trading, technology bubble, the market place, Thomas Kuhn: the structure of scientific revolutions, transaction costs, value at risk, yield curve, zero-sum game

For example, Surplus-at-Risk (SAR)/Liability Driven Investment (LDI) often seeks to minimize risk relative to liabilities, rather than broad, return based benchmarks with the goal of delivering nominal, inflation-linked, or wagelinked defined benefits. VALUE AT RISK A chapter on risk and what it is would not be complete (and it never is) without a mention of the concept of “value at risk” or VaR. For a given portfolio, probability, and time horizon, VaR is defined as the loss that is expected to be exceeded with the given probability, over the given time horizon under normal market conditions assuming that there is no portfolio rebalancing. For example, if a portfolio of stocks has a one-day VaR of $1 million at the 95% confidence level, then there is 5% chance that the one- 35 Measuring Risk day loss of the portfolio could exceed $1 million assuming normal market conditions and no intra day rebalancing.

Further, as we have also argued, the performance of a diversified portfolio is mostly determined by its exposures to various sources of risk. In this section, we use value at risk (VaR) to measure a portfolio’s overall risk. Then we show how the VaR of a portfolio can be decomposed so one could know how allocation to each asset class contributes to the total risk of the portfolio. In this way, the portfolio manager can balance the potential return from each allocation by the contribution of the allocation to the total risk of the portfolio. As was pointed out in Chapter 2, the VaR of a portfolio measures its potential losses due to market risks. In particular, the daily VaR of a portfolio at the confidence level of α states that the portfolio will not suffer a loss greater than VaR with probability of α.

How to educate, how to inform, how to reeducate, and how to reinform is the struggle for the next decade. ■ ■ NOTE 1. There is considerable research on alternative means of tracking and evaluating the potential volatility of existing fund strategies and overall portfolio risk. The generic term given to such analysis often falls under the classification of VaR (value at risk), which often offers a simplified forecast of the probability of losing more than x dollars of asset value. The entire area of monitoring and evaluating fund risk is constantly evolving, and readers are directed to articles in academic (The Journal of Alternative Investments) and practitioner press to track changes and advances in the field.


pages: 381 words: 101,559

Currency Wars: The Making of the Next Gobal Crisis by James Rickards

"World Economic Forum" Davos, Alan Greenspan, Asian financial crisis, bank run, Bear Stearns, behavioural economics, Benoit Mandelbrot, Berlin Wall, Big bang: deregulation of the City of London, Black Swan, borderless world, Bretton Woods, BRICs, British Empire, business climate, buy and hold, capital controls, Carmen Reinhart, Cass Sunstein, collateralized debt obligation, complexity theory, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, cross-border payments, currency manipulation / currency intervention, currency peg, currency risk, Daniel Kahneman / Amos Tversky, deal flow, Deng Xiaoping, diversification, diversified portfolio, Dr. Strangelove, Fall of the Berlin Wall, family office, financial innovation, floating exchange rates, full employment, game design, German hyperinflation, Gini coefficient, global rebalancing, global reserve currency, Great Leap Forward, guns versus butter model, high net worth, income inequality, interest rate derivative, it's over 9,000, John Meriwether, Kenneth Rogoff, laissez-faire capitalism, liquidity trap, Long Term Capital Management, low interest rates, mandelbrot fractal, margin call, market bubble, Mexican peso crisis / tequila crisis, Money creation, money market fund, money: store of value / unit of account / medium of exchange, Myron Scholes, Network effects, New Journalism, Nixon shock, Nixon triggered the end of the Bretton Woods system, offshore financial centre, oil shock, one-China policy, open economy, paradox of thrift, Paul Samuelson, power law, price mechanism, price stability, private sector deleveraging, proprietary trading, quantitative easing, race to the bottom, RAND corporation, rent-seeking, reserve currency, Ronald Reagan, short squeeze, sovereign wealth fund, special drawing rights, special economic zone, subprime mortgage crisis, The Myth of the Rational Market, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, time value of money, too big to fail, value at risk, vertical integration, War on Poverty, Washington Consensus, zero-sum game

One contagious virus that spread the financial economics disease was known as value at risk, or VaR. Value at risk is the method Wall Street used to manage risk in the decade leading up to the Panic of 2008 and it is still in widespread use today. It is a way to measure risk in an overall portfolio—certain risky positions are offset against other positions to reduce risk, and VaR claims to measure that offset. For example, a long position in ten-year Treasury notes might be offset by a short position in five-year Treasury notes so that the net risk, according to VaR, is much less than either of the separate risks of the notes.

The mathematics quickly become daunting, because clear relationships such as longs and shorts in the same bond give way to the multiple relationships of many items in the hedging basket. Value at risk is the mathematical culmination of fifty years of financial economics. Importantly, it assumes that future relationships between prices will resemble the past. VaR assumes that price fluctuations are random and that risk is embedded in net positions—long minus short—instead of gross positions. VaR carries the intellectual baggage of efficient markets and normal distributions into the world of risk management. The role of VaR in causing the Panic of 2008 is immense but has never been thoroughly explored.

The highly conflicted and fraudulent roles of mortgage brokers, investment bankers and ratings agencies have been extensively examined. Yet the role of VaR has remained hidden. In many ways, VaR was the invisible thread that ran through all the excesses that led to the collapse. What was it that allowed the banks, ratings agencies and investors to assume that their positions were safe? What was it that gave the Federal Reserve and the SEC comfort that the banks and brokers had adequate capital? Why did bank risk managers continually assure their CEOs and boards of directors that everything was under control? The answers revolve around value at risk and its related models. The VaR models gave the all clear to higher leverage and massive off–balance sheet exposures.


pages: 1,082 words: 87,792

Python for Algorithmic Trading: From Idea to Cloud Deployment by Yves Hilpisch

algorithmic trading, Amazon Web Services, automated trading system, backtesting, barriers to entry, bitcoin, Brownian motion, cloud computing, coronavirus, cryptocurrency, data science, deep learning, Edward Thorp, fiat currency, global macro, Gordon Gekko, Guido van Rossum, implied volatility, information retrieval, margin call, market microstructure, Myron Scholes, natural language processing, paper trading, passive investing, popular electronics, prediction markets, quantitative trading / quantitative finance, random walk, risk free rate, risk/return, Rubik’s Cube, seminal paper, Sharpe ratio, short selling, sorting algorithm, systematic trading, transaction costs, value at risk

., 2.5, 5.0, 10.0] In [99]: risk['return'] = np.log(risk['equity'] / risk['equity'].shift(1)) In [100]: VaR = scs.scoreatpercentile(equity * risk['return'], percs) In [101]: def print_var(): print('{} {}'.format('Confidence Level', 'Value-at-Risk')) print(33 * '-') for pair in zip(percs, VaR): print('{:16.2f} {:16.3f}'.format(100 - pair[0], -pair[1])) In [102]: print_var() Confidence Level Value-at-Risk --------------------------------- 99.99 162.570 99.90 161.348 99.00 132.382 97.50 122.913 95.00 100.950 90.00 62.622 Defines the percentile values to be used. Calculates the VaR values given the percentile values. Translates the percentile values into confidence levels and the VaR values (negative values) to positive values for printing.

Translates the percentile values into confidence levels and the VaR values (negative values) to positive values for printing. Finally, the following code calculates the VaR values for a time horizon of one hour by resampling the original DataFrame object. In effect, the VaR values are increased for all confidence levels: In [103]: hourly = risk.resample('1H', label='right').last() In [104]: hourly['return'] = np.log(hourly['equity'] / hourly['equity'].shift(1)) In [105]: VaR = scs.scoreatpercentile(equity * hourly['return'], percs) In [106]: print_var() Confidence Level Value-at-Risk --------------------------------- 99.99 252.460 99.90 251.744 99.00 244.593 97.50 232.674 95.00 125.498 90.00 61.701 Resamples the data from 10-minute to 1-hour bars.

Calculates the timedelta values between all highs. The longest drawdown period in seconds… …transformed to hours. Another important risk measure is value-at-risk (VaR). It is quoted as a currency amount and represents the maximum loss to be expected given both a certain time horizon and a confidence level. Figure 10-7. Maximum drawdown (vertical line) and drawdown periods (horizontal lines) The following code derives VaR values based on the log returns of the equity position for the leveraged trading strategy over time for different confidence levels. The time interval is fixed to the bar length of ten minutes: In [97]: import scipy.stats as scs In [98]: percs = [0.01, 0.1, 1., 2.5, 5.0, 10.0] In [99]: risk['return'] = np.log(risk['equity'] / risk['equity'].shift(1)) In [100]: VaR = scs.scoreatpercentile(equity * risk['return'], percs) In [101]: def print_var(): print('{} {}'.format('Confidence Level', 'Value-at-Risk')) print(33 * '-') for pair in zip(percs, VaR): print('{:16.2f} {:16.3f}'.format(100 - pair[0], -pair[1])) In [102]: print_var() Confidence Level Value-at-Risk --------------------------------- 99.99 162.570 99.90 161.348 99.00 132.382 97.50 122.913 95.00 100.950 90.00 62.622 Defines the percentile values to be used.


pages: 413 words: 117,782

What Happened to Goldman Sachs: An Insider's Story of Organizational Drift and Its Unintended Consequences by Steven G. Mandis

activist fund / activist shareholder / activist investor, algorithmic trading, Bear Stearns, Berlin Wall, Bob Litterman, bonus culture, book value, BRICs, business process, buy and hold, Carl Icahn, collapse of Lehman Brothers, collateralized debt obligation, commoditize, complexity theory, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, disintermediation, diversification, eat what you kill, Emanuel Derman, financial innovation, fixed income, friendly fire, Glass-Steagall Act, Goldman Sachs: Vampire Squid, high net worth, housing crisis, junk bonds, London Whale, Long Term Capital Management, merger arbitrage, Myron Scholes, new economy, passive investing, performance metric, proprietary trading, radical decentralization, risk tolerance, Ronald Reagan, Saturday Night Live, Satyajit Das, shareholder value, short selling, sovereign wealth fund, subprime mortgage crisis, systems thinking, The Nature of the Firm, too big to fail, value at risk

As mentioned earlier, Goldman had learned from its 1994 experience. Value at Risk, Models, and Risk Management Models are widely used in risk management to synthesize risk and help analysts, investors, and company boards determine acceptable trading parameters under different scenarios. Value at risk (VaR) is a widely used measure of the risk of loss on a specific portfolio of financial assets, expressed in terms of a probability of losing a given percentage of the value of a portfolio—in mark-to-market value—over a certain time. For example, if a portfolio of stocks has a one-day 5 percent VaR of $1 million, there is a 0.05 probability that the portfolio will fall in value by more than $1 million over a one-day period.

Interviews confirmed the level of dissonance at Goldman, even as a publicly traded firm, in discussing and understanding that the output of the models was and is unique to Goldman, which meant the firm was not as dependent on the models as were other firms, and that, combined with what sociologists call a “heterarchical structure” (less hierarchy in the chain of command than in many firms) and the trading experience of its top executives, gave Goldman an edge.8 The more intense scrutiny of the models and risk factors led Goldman’s top executives to pick up on market signals that other firms’ executives missed.9 As Emanuel Derman, the former head of the quantitative risk strategies group at Goldman and now a professor at Columbia, wrote, at Goldman, “Even if you insist on representing risk with a single number, VaR isn’t the best one … As a result, though we [Goldman] used VaR, we didn’t make it our religion.”10 (Meanwhile, at other firms, measures like “VaR [value at risk] … became institutionalized,” as the New York Times’ Joe Nocera put it. “Corporate chieftains like Stanley O’Neal at Merrill Lynch and Charles Prince at Citigroup pushed their divisions to take more risk because they were being left behind in the race for trading profits. All over Wall Street, VaR numbers increased.”11) Even though VaR has flaws, it is the only relatively consistent risk data that is publicly reported from the various banks, which is why I analyzed it.

Informally, a loss of $1 million or more on this portfolio is expected on one day in twenty. Typically, banks report the VaR by risk type (e.g., interest rates, equity prices, currency rates, and commodity prices). VaR may be an unsatisfactory risk metric, but it has become an industry standard. Wall Street equity analysts expect banks to provide risk (VaR) calculations quarterly, and they talk about risk increasing, or decreasing, depending on the output of the models. The models and VaR calculations, however, make numerous assumptions, some of which proved over time to be invalid, making it dangerous to rely on or extrapolate too much on VaR. Analysts and investors (and boards of directors) overrely on VAR as a measurement of risk, and therefore management teams do also, one of the external influences of being a public company.


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Inside the House of Money: Top Hedge Fund Traders on Profiting in a Global Market by Steven Drobny

Abraham Maslow, Alan Greenspan, Albert Einstein, asset allocation, Berlin Wall, Bonfire of the Vanities, Bretton Woods, business cycle, buy and hold, buy low sell high, capital controls, central bank independence, commoditize, commodity trading advisor, corporate governance, correlation coefficient, Credit Default Swap, currency risk, diversification, diversified portfolio, family office, financial engineering, fixed income, glass ceiling, Glass-Steagall Act, global macro, Greenspan put, high batting average, implied volatility, index fund, inflation targeting, interest rate derivative, inventory management, inverted yield curve, John Meriwether, junk bonds, land bank, Long Term Capital Management, low interest rates, managed futures, margin call, market bubble, Market Wizards by Jack D. Schwager, Maui Hawaii, Mexican peso crisis / tequila crisis, moral hazard, Myron Scholes, new economy, Nick Leeson, Nixon triggered the end of the Bretton Woods system, oil shale / tar sands, oil shock, out of africa, panic early, paper trading, Paul Samuelson, Peter Thiel, price anchoring, proprietary trading, purchasing power parity, Reminiscences of a Stock Operator, reserve currency, risk free rate, risk tolerance, risk-adjusted returns, risk/return, rolodex, Sharpe ratio, short selling, Silicon Valley, tail risk, The Wisdom of Crowds, too big to fail, transaction costs, value at risk, Vision Fund, yield curve, zero-coupon bond, zero-sum game

We also use a subjective limit of “If this happens, we get out.”We manage all of our position sizes individually, then aggregate them based on worst-case moves. We look at value at risk (VAR), but if there’s a very high VAR I know I have much larger risk on because VAR is dampening. In a concentrated portfolio, like a lot of commodities portfolios, I think it’s incredibly dangerous to manage off a VAR number. If you have a thousand traders, like a Goldman Sachs, a VAR calculation can make sense because you have enough diversification to give you something statistically significant. VAR assumes that there is some positive/negative correlation between commodities. If I am long $100 of aluminum and short $100 of copper, and copper is more volatile, it would show me that I have a net risk of, say, $20 from the copper, choosing an arbitrary dollar amount.

Our risk is not limited to Barclays’ outstanding liabilities.We are actively managing risk and seeking a positive absolute return while being limited by the firm’s value at risk (VAR) model, regulatory capital limits, and balance sheet limits. We look to maximize current income for a given unit of risk. As a result, we tend to be in the front end of the yield curve as opposed to the back end because it’s better to roll one billion one-year notes for 10 years than to buy 100 million 10-year bonds ceteris paribus.The VAR would be the same if they had the same volatility but with the one-year notes, you get much more current income. By concentrating risk in the front end of the yield curve, the only thing that can really make me right or wrong is a central bank.

For example, in the fourth quarter of 2004 we recognized the growing imbalances in the United States and the need for a higher savings rate and thus a weaker dollar.We looked at those macro factors and positioned ourselves through foreign exchange and bonds. Foreign exchange was clearly the best trade, but again, we’re not really a global macro fund so it’s hard for us to have all of our risk in foreign exchange. In this example, we put a total of 2 percent of our value at risk (VAR) THE FIXED INCOME SPECIALISTS 317 into that macro view, with 60 basis points of VAR going toward short USD, long euro FX, and 70 basis points each in long European bonds and short U.S. bonds. This trade structure captured the idea of slow domestic demand in Europe, higher short rates in the United States, and a weaker dollar.We expressed quite a lot of the bond side through options because European interest rate volatility was very cheap.


The Trade Lifecycle: Behind the Scenes of the Trading Process (The Wiley Finance Series) by Robert P. Baker

asset-backed security, bank run, banking crisis, Basel III, Black-Scholes formula, book value, Brownian motion, business continuity plan, business logic, business process, collapse of Lehman Brothers, corporate governance, credit crunch, Credit Default Swap, diversification, financial engineering, fixed income, functional programming, global macro, hiring and firing, implied volatility, interest rate derivative, interest rate swap, locking in a profit, London Interbank Offered Rate, low interest rates, margin call, market clearing, millennium bug, place-making, prediction markets, proprietary trading, short selling, statistical model, stochastic process, the market place, the payments system, time value of money, too big to fail, transaction costs, value at risk, Wiener process, yield curve, zero-coupon bond

The purpose of scenario analysis is to measure the risk caused by something unusual but not impossible occurring and to factor in some correlation between different types of market risk (as opposed to sensitivity analysis which perturbs each set of market data independently). Value at Risk (VaR) VaR is another risk measure. It attempts to state the maximum loss that will occur within a period of time. Since the maximum loss is in effect unlimited, the VaR puts a probability on the maximum loss occurring. For example, there is a 95% probability that the maximum one-day loss will be 12 million euros. In other words, 19 times out of 20 the loss will not exceed 12 million euros. The converse is that once every 20 times (about once a month) the loss will be more. It may not be very comforting to the board of a firm to know that VaR is likely to be exceeded once a month, but it is an industry standard measure and helps to make comparisons with other organisations to compare their risk exposures.

This lends itself to scenario analysis which, in essence, is a process of generating the market data you want to use, valuing with that market data and comparing the results. Some typical scenarios: Bump all market data up by 5%. Set all market data to be at its lowest value for the past month. Set volatility of spot prices to 15%. Assume all credit recovery rates fall to 5%. Value at Risk (VaR) VaR is a type of worst-case valuation – over the next day (week, month, …) with an unchanged portfolio asking how bad could the valuation get? Typically this is done on a portfolio basis and requires, in addition to a valuation model, a model of the variability (volatility) of the driving variables (interest and exchange rates, equity prices, etc.) and their relationship (correlation).

The overall effect should be to reduce benefits of internal models and increase banks’ costs. Also, by increasing the capital required, banks will be driven to reassess pricing and whether it is worth continuing to offer certain products. In October 2013, the Basel Committee published a paper proposing: a move from the standard Value at Risk (VaR) calculation to that of Expected Shortfall. This will increase capital charges simpler boundary between the trading and banking book extending time horizons for liquidation of exposures in stressed market conditions a tougher approach to allowing benefits of hedging. 206 THE TRADE LIFECYCLE Effect of regulation in practice Naturally the actual business of trading is impacted by the weight of regulation.


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Fortune's Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street by William Poundstone

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", Albert Einstein, anti-communist, asset allocation, Bear Stearns, beat the dealer, Benoit Mandelbrot, Black Monday: stock market crash in 1987, Black-Scholes formula, Bletchley Park, Brownian motion, buy and hold, buy low sell high, capital asset pricing model, Claude Shannon: information theory, computer age, correlation coefficient, diversified portfolio, Edward Thorp, en.wikipedia.org, Eugene Fama: efficient market hypothesis, financial engineering, Henry Singleton, high net worth, index fund, interest rate swap, Isaac Newton, Johann Wolfgang von Goethe, John Meriwether, John von Neumann, junk bonds, Kenneth Arrow, Long Term Capital Management, Louis Bachelier, margin call, market bubble, market fundamentalism, Marshall McLuhan, Michael Milken, Myron Scholes, New Journalism, Norbert Wiener, offshore financial centre, Paul Samuelson, publish or perish, quantitative trading / quantitative finance, random walk, risk free rate, risk tolerance, risk-adjusted returns, Robert Shiller, Ronald Reagan, Rubik’s Cube, short selling, speech recognition, statistical arbitrage, Teledyne, The Predators' Ball, The Wealth of Nations by Adam Smith, transaction costs, traveling salesman, value at risk, zero-coupon bond, zero-sum game

Looking at the numbers would reassure the execs that the bank was not assuming too much risk. Two of Morgan’s analysts, Til Guldimann and Jacques Longerstaey, devised Value at Risk. The concept is as simple as it can be. Compute how much a portfolio stands to lose within a given time frame, and with what probability. A VaR report might say that there is a 1-in-20 chance that a portfolio will lose $1.64 million or more in the next day of trading. Want more numbers? VaR’s got as many numbers as you want. Make a spreadsheet. The cells of the spreadsheet are the possible losses, for different time periods or various thresholds of likelihood.

They have failed at the most important judgment a trader can make, namely how much money to commit to a risky trade. LTCM’s people were well aware that multiplying profits through leverage also multiplies risk of ruin. They told investors that they had risk under control through their financial engineering. LTCM used a sophisticated form of the industry standard risk reporting system, VaR or “Value at Risk.” After the Black Monday crash of 1987, investment bank J. P. Morgan became concerned with getting a handle on risk. Derivatives, interest rate swaps, and repurchase agreements had changed the financial landscape so much that it was no longer a simple thing for a bank executive (much less a client) to understand what risks the people in the firm were taking.

Should something terrible happen later on, the money manager can always pull out the VaR report, point to cell D18, the 5 percent risk of a 37 percent loss. As a ritual between portfolio manager and client, calculating VaR is not such a bad idea in a litigious society where many well-off people don’t know much math. In October 1994, LTCM sent its investors a document comparing projected returns to risks. One reported factoid: In order to make a 25 percent annual return, the fund would have to assume a 1 percent chance of losing 20 percent or more of the fund’s value in a year. A 20-percent-or-more loss was the worst case considered. The chapter on Value at Risk in the popular finance textbook Paul Wilmott Introduces Quantitative Finance begins with a cartoon of the author shrugging.


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Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined by Lasse Heje Pedersen

activist fund / activist shareholder / activist investor, Alan Greenspan, algorithmic trading, Andrei Shleifer, asset allocation, backtesting, bank run, banking crisis, barriers to entry, Bear Stearns, behavioural economics, Black-Scholes formula, book value, Brownian motion, business cycle, buy and hold, buy low sell high, buy the rumour, sell the news, capital asset pricing model, commodity trading advisor, conceptual framework, corporate governance, credit crunch, Credit Default Swap, currency peg, currency risk, David Ricardo: comparative advantage, declining real wages, discounted cash flows, diversification, diversified portfolio, Emanuel Derman, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, financial engineering, fixed income, Flash crash, floating exchange rates, frictionless, frictionless market, global macro, Gordon Gekko, implied volatility, index arbitrage, index fund, interest rate swap, junk bonds, late capitalism, law of one price, Long Term Capital Management, low interest rates, managed futures, margin call, market clearing, market design, market friction, Market Wizards by Jack D. Schwager, merger arbitrage, money market fund, mortgage debt, Myron Scholes, New Journalism, paper trading, passive investing, Phillips curve, price discovery process, price stability, proprietary trading, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, Renaissance Technologies, Richard Thaler, risk free rate, risk-adjusted returns, risk/return, Robert Shiller, selection bias, shareholder value, Sharpe ratio, short selling, short squeeze, SoftBank, sovereign wealth fund, statistical arbitrage, statistical model, stocks for the long run, stocks for the long term, survivorship bias, systematic trading, tail risk, technology bubble, time dilation, time value of money, total factor productivity, transaction costs, two and twenty, value at risk, Vanguard fund, yield curve, zero-coupon bond

Another measure of risk is value-at-risk (VaR), which attempts to capture tail risk (non-normality). The VaR measures the maximum loss with a certain confidence, as seen in figure 4.1 below. For example, the VaR is the most that you can lose with a 95% or 99% confidence. For instance, a hedge fund has a one-day 95% VaR of $10 million if A simple way to estimate VaR is to line up past returns, sort them by magnitude, and find a return that has 5% worse days and 95% better days. This is the 95% VaR, since, if history repeats itself, you will lose less than this number with 95% certainty. You can estimate the VaR by looking at your past returns, but if your positions have changed a lot, this can be rather misleading.

In that case, it may be more accurate to look at your current positions and simulate returns on these positions over, say, the past three years. Figure 4.1. Value-at-risk. The x-axis has the possible outcomes for the return, and the y-axis has the corresponding probability density. One issue with the VaR is that it does not depend on how much you lose if you do lose more than the VaR. The magnitude of these extreme tail losses is, in principle, captured by the risk measure called the expected shortfall (ES). The expected shortfall is the expected loss, given that you are losing more than the VaR: Another measure of risk is the stress loss. This measure is computed by performing various stress tests, that is, simulated portfolio returns during various scenarios, and then considering the worst-case loss in these scenarios.

To capture crash risk, the denominator is modified in performance measures such as the risk-adjusted return on capital (RAROC): where economic capital is the amount of capital that you need to set aside to sustain worst-case losses on the strategy with a certain confidence. Hence, the denominator is the crash risk instead of day-to-day swings. Economic capital can be estimated using value-at-risk (VaR) or stress tests, concepts that we discuss in detail in section 4.2 on risk management. The Sortino ratio (S) uses downside risk: The downside risk (or downside deviation) is calculated as the standard deviation of returns truncated above some minimum acceptable return (MAR): The MAR is often set to be the risk-free rate or zero.


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Traders, Guns & Money: Knowns and Unknowns in the Dazzling World of Derivatives by Satyajit Das

accounting loophole / creative accounting, Alan Greenspan, Albert Einstein, Asian financial crisis, asset-backed security, Bear Stearns, beat the dealer, Black Swan, Black-Scholes formula, Bretton Woods, BRICs, Brownian motion, business logic, business process, buy and hold, buy low sell high, call centre, capital asset pricing model, collateralized debt obligation, commoditize, complexity theory, computerized trading, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, currency peg, currency risk, disinformation, disintermediation, diversification, diversified portfolio, Edward Thorp, Eugene Fama: efficient market hypothesis, Everything should be made as simple as possible, financial engineering, financial innovation, fixed income, Glass-Steagall Act, Haight Ashbury, high net worth, implied volatility, index arbitrage, index card, index fund, interest rate derivative, interest rate swap, Isaac Newton, job satisfaction, John Bogle, John Meriwether, junk bonds, locking in a profit, Long Term Capital Management, low interest rates, mandelbrot fractal, margin call, market bubble, Marshall McLuhan, mass affluent, mega-rich, merger arbitrage, Mexican peso crisis / tequila crisis, money market fund, moral hazard, mutually assured destruction, Myron Scholes, new economy, New Journalism, Nick Leeson, Nixon triggered the end of the Bretton Woods system, offshore financial centre, oil shock, Parkinson's law, placebo effect, Ponzi scheme, proprietary trading, purchasing power parity, quantitative trading / quantitative finance, random walk, regulatory arbitrage, Right to Buy, risk free rate, risk-adjusted returns, risk/return, Salesforce, Satyajit Das, shareholder value, short selling, short squeeze, South Sea Bubble, statistical model, technology bubble, the medium is the message, the new new thing, time value of money, too big to fail, transaction costs, value at risk, Vanguard fund, volatility smile, yield curve, Yogi Berra, zero-coupon bond

What did the number actually mean? What was it used for? The answers to these questions are inevitably vague. Like religious matters, faith is key. The reader of the Report had no way of verifying whether it was correct. You have to believe in the thing. The holy liturgy of risk is built around a concept known as VAR – ‘value at risk’. Sceptics refer to it as ‘Variable And wRong’. There is also DEAR – ‘daily earning at risk’. The concepts all go back to Carl Frederich Gauss, a nineteenth century German mathematician of rare genius. The Gaussian distribution lies at the centre of modern finance, especially risk management and financial modelling.

However, the text is different. 6 ‘What Worries Warren’ (3 March 2003) Fortune. 13_INDEX.QXD 17/2/06 4:44 pm Page 325 Index accounting rules 139, 221, 228, 257 Accounting Standards Board 33 accrual accounting 139 active fund management 111 actuaries 107–10, 205, 289 Advance Corporation Tax 242 agency business 123–4, 129 agency theory 117 airline profits 140–1 Alaska 319 Allen, Woody 20 Allied Irish Bank 143 Allied Lyons 98 alternative investment strategies 112, 308 American Express 291 analysts, role of 62–4 anchor effect 136 Anderson, Rolf 92–4 annuities 204–5 ANZ Bank 277 Aquinas, Thomas 137 arbitrage 33, 38–40, 99, 114, 137–8, 171–2, 245–8, 253–5, 290, 293–6 arbitration 307 Argentina 45 arithmophobia 177 ‘armpit theory’ 303 Armstrong World Industries 274 arrears assets 225 Ashanti Goldfields 97–8, 114 Asian financial crisis (1997) 4, 9, 44–5, 115, 144, 166, 172, 207, 235, 245, 252, 310, 319 asset consultants 115–17, 281 ‘asset growth’ strategy 255 asset swaps 230–2 assets under management (AUM) 113–4, 117 assignment of loans 267–8 AT&T 275 attribution of earnings 148 auditors 144 Australia 222–4, 254–5, 261–2 back office functions 65–6 back-to-back loans 35, 40 backwardation 96 Banca Popolare di Intra 298 Bank of America 298, 303 Bank of International Settlements 50–1, 281 Bank of Japan 220 Bankers’ Trust (BT) 59, 72, 101–2, 149, 217–18, 232, 268–71, 298, 301, 319 banking regulations 155, 159, 162, 164, 281, 286, 288 banking services 34; see also commercial banks; investment banks bankruptcy 276–7 Banque Paribas 37–8, 232 Barclays Bank 121–2, 297–8 13_INDEX.QXD 17/2/06 326 4:44 pm Page 326 Index Baring, Peter 151 Baring Brothers 51, 143, 151–2, 155 ‘Basel 2’ proposal 159 basis risk 28, 42, 274 Bear Stearns 173 bearer eurodollar collateralized securities (BECS) 231–3 ‘behavioural finance’ 136 Berkshire Hathaway 19 Bermudan options 205, 227 Bernstein, Peter 167 binomial option pricing model 196 Bismarck, Otto von 108 Black, Fischer 22, 42, 160, 185, 189–90, 193, 195, 197, 209, 215 Black–Scholes formula for option pricing 22, 185, 194–5 Black–Scholes–Merton model 160, 189–93, 196–7 ‘black swan’ hypothesis 130 Blair, Tony 223 Bogle, John 116 Bohr, Niels 122 Bond, Sir John 148 ‘bond floor’ concept 251–4 bonding 75–6, 168, 181 bonuses 146–51, 244, 262, 284–5 Brady Commission 203 brand awareness and brand equity 124, 236 Brazil 302 Bretton Woods system 33 bribery 80, 303 British Sky Broadcasting (BSB) 247–8 Brittain, Alfred 72 broad index secured trust offerings (BISTROs) 284–5 brokers 69, 309 Brown, Robert 161 bubbles 210, 310, 319 Buconero 299 Buffet, Warren 12, 19–20, 50, 110–11, 136, 173, 246, 316 business process reorganization 72 business risk 159 Business Week 130 buy-backs 249 ‘call’ options 25, 90, 99, 101, 131, 190, 196 callable bonds 227–9, 256 capital asset pricing model (CAPM) 111 capital flow 30 capital guarantees 257–8 capital structure arbitrage 296 Capote, Truman 87 carbon trading 320 ‘carry cost’ model 188 ‘carry’ trades 131–3, 171 cash accounting 139 catastrophe bonds 212, 320 caveat emptor principle 27, 272 Cayman Islands 233–4 Cazenove (company) 152 CDO2 292 Cemex 249–50 chaos theory 209, 312 Chase Manhattan Bank 143, 299 Chicago Board Options Exchange 195 Chicago Board of Trade (CBOT) 25–6, 34 chief risk officers 177 China 23–5, 276, 302–4 China Club, Hong Kong 318 Chinese walls 249, 261, 280 chrematophobia 177 Citibank and Citigroup 37–8, 43, 71, 79, 94, 134–5, 149, 174, 238–9 Citron, Robert 124–5, 212–17 client relationships 58–9 Clinton, Bill 223 Coats, Craig 168–9 collateral requirements 215–16 collateralized bond obligations (CBOs) 282 collateralized debt obligations (CDOs) 45, 282–99 13_INDEX.QXD 17/2/06 4:44 pm Page 327 Index collateralized fund obligations (CFOs) 292 collateralized loan obligations (CLOs) 283–5, 288 commercial banks 265–7 commoditization 236 commodity collateralized obligations (CCOs) 292 commodity prices 304 Commonwealth Bank of Australia 255 compliance officers 65 computer systems 54, 155, 197–8 concentration risk 271, 287 conferences with clients 59 confidence levels 164 confidentiality 226 Conseco 279–80 contagion crises 291 contango 96 contingent conversion convertibles (co-cos) 257 contingent payment convertibles (co-pays) 257 Continental Illinois 34 ‘convergence’ trading 170 convertible bonds 250–60 correlations 163–6, 294–5; see also default correlations corruption 303 CORVUS 297 Cox, John 196–7 credit cycle 291 credit default swaps (CDSs) 271–84, 293, 299 credit derivatives 129, 150, 265–72, 282, 295, 299–300 Credit Derivatives Market Practices Committee 273, 275, 280–1 credit models 294, 296 credit ratings 256–7, 270, 287–8, 297–8, 304 credit reserves 140 credit risk 158, 265–74, 281–95, 299 327 credit spreads 114, 172–5, 296 Credit Suisse 70, 106, 167 credit trading 293–5 CRH Capital 309 critical events 164–6 Croesus 137 cross-ruffing 142 cubic splines 189 currency options 98, 218, 319 custom repackaged asset vehicles (CRAVEs) 233 daily earning at risk (DEAR) concept 160 Daiwa Bank 142 Daiwa Europe 277 Danish Oil and Natural Gas 296 data scrubbing 142 dealers, work of 87–8, 124–8, 133, 167, 206, 229–37, 262, 295–6; see also traders ‘death swap’ strategy 110 decentralization 72 decision-making, scientific 182 default correlations 270–1 defaults 277–9, 287, 291, 293, 296, 299 DEFCON scale 156–7 ‘Delta 1’ options 243 delta hedging 42, 200 Deming, W.E. 98, 101 Denmark 38 deregulation, financial 34 derivatives trading 5–6, 12–14, 18–72, 79, 88–9, 99–115, 123–31, 139–41, 150, 153, 155, 175, 184–9, 206–8, 211–14, 217–19, 230, 233, 257, 262–3, 307, 316, 319–20; see also equity derivatives Derman, Emmanuel 185, 198–9 Deutsche Bank 70, 104, 150, 247–8, 274, 277 devaluations 80–1, 89, 203–4, 319 13_INDEX.QXD 17/2/06 4:44 pm Page 328 328 Index dilution of share capital 241 DINKs 313 Disney Corporation 91–8 diversification 72, 110–11, 166, 299 dividend yield 243 ‘Dr Evil’ trade 135 dollar premium 35 downsizing 73 Drexel Burnham Lambert (DBL) 282 dual currency bonds 220–3; see also reverse dual currency bonds earthquakes, bonds linked to 212 efficient markets hypothesis 22, 31, 111, 203 electronic trading 126–30, 134 ‘embeddos’ 218 emerging markets 3–4, 44, 115, 132–3, 142, 212, 226, 297 Enron 54, 142, 250, 298 enterprise risk management (ERM) 176 equity capital management 249 equity collateralized obligations (ECOs) 292 equity derivatives 241–2, 246–9, 257–62 equity index 137–8 equity investment, retail market in 258–9 equity investors’ risk 286–8 equity options 253–4 equity swaps 247–8 euro currency 171, 206, 237 European Bank for Reconstruction and Development 297 European currency units 93 European Union 247–8 Exchange Rate Mechanism, European 204 exchangeable bonds 260 expatriate postings 81–2 expert witnesses 310–12 extrapolation 189, 205 extreme value theory 166 fads of management science 72–4 ‘fairway bonds’ 225 Fama, Eugene 22, 111, 194 ‘fat tail’ events 163–4 Federal Accounting Standards Board 266 Federal Home Loans Bank 213 Federal National Mortgage Association 213 Federal Reserve Bank 20, 173 Federal Reserve Board 132 ‘Ferraris’ 232 financial engineering 228, 230, 233, 249–50, 262, 269 Financial Services Authority (FSA), Japan 106, 238 Financial Services Authority (FSA), UK 15, 135 firewalls 235–6 firing of staff 84–5 First Interstate Ltd 34–5 ‘flat’ organizations 72 ‘flat’ positions 159 floaters 231–2; see also inverse floaters ‘flow’ trading 60–1, 129 Ford Motors 282, 296 forecasting 135–6, 190 forward contracts 24–33, 90, 97, 124, 131, 188 fugu fish 239 fund management 109–17, 286, 300 futures see forward contracts Galbraith, John Kenneth 121 gamma risk 200–2, 294 Gauss, Carl Friedrich 160–2 General Motors 279, 296 General Reinsurance 20 geometric Brownian motion (GBM) 161 Ghana 98 Gibson Greeting Cards 44 Glass-Steagall Act 34 gold borrowings 132 13_INDEX.QXD 17/2/06 4:44 pm Page 329 Index gold sales 97, 137 Goldman Sachs 34, 71, 93, 150, 173, 185 ‘golfing holiday bonds’ 224 Greenspan, Alan 6, 9, 19–21, 29, 43, 47, 50, 53, 62, 132, 159, 170, 215, 223, 308 Greenwich NatWest 298 Gross, Bill 19 Guangdong International Trust and Investment Corporation (GITIC) 276–7 guaranteed annuity option (GAO) contracts 204–5 Gutenfreund, John 168–9 gyosei shido 106 Haghani, Victor 168 Hamanaka, Yasuo 142 Hamburgische Landesbank 297 Hammersmith and Fulham, London Borough of 66–7 ‘hara-kiri’ swaps 39 Hartley, L.P. 163 Hawkins, Greg 168 ‘heaven and hell’ bonds 218 hedge funds 44, 88–9, 113–14, 167, 170–5, 200–2, 206, 253–4, 262–3, 282, 292, 296, 300, 308–9 hedge ratio 264 hedging 24–8, 31, 38–42, 60, 87–100, 184, 195–200, 205–7, 214, 221, 229, 252, 269, 281, 293–4, 310 Heisenberg, Werner 122 ‘hell bonds’ 218 Herman, Clement (‘Crem’) 45–9, 77, 84, 309 Herodotus 137, 178 high net worth individuals (HNWIs) 237–8, 286 Hilibrand, Lawrence 168 Hill Samuel 231–2 329 The Hitchhiker’s Guide to the Galaxy 189 Homer, Sidney 184 Hong Kong 9, 303–4 ‘hot tubbing’ 311–12 HSBC Bank 148 HSH Nordbank 297–8 Hudson, Kevin 102 Hufschmid, Hans 77–8 IBM 36, 218, 260 ICI 34 Iguchi, Toshihude 142 incubators 309 independent valuation 142 indexed currency option notes (ICONs) 218 India 302 Indonesia 5, 9, 19, 26, 55, 80–2, 105, 146, 219–20, 252, 305 initial public offerings 33, 64, 261 inside information and insider trading 133, 241, 248–9 insurance companies 107–10, 117, 119, 150, 192–3, 204–5, 221, 223, 282, 286, 300; see also reinsurance companies insurance law 272 Intel 260 intellectual property in financial products 226 Intercontinental Hotels Group (IHG) 285–6 International Accounting Standards 33 International Securities Market Association 106 International Swap Dealers Association (ISDA) 273, 275, 279, 281 Internet stock and the Internet boom 64, 112, 259, 261, 310, 319 interpolation of interest rates 141–2, 189 inverse floaters 46–51, 213–16, 225, 232–3 13_INDEX.QXD 17/2/06 4:44 pm Page 330 330 Index investment banks 34–8, 62, 64, 67, 71, 127–8, 172, 198, 206, 216–17, 234, 265–7, 298, 309 investment managers 43–4 investment styles 111–14 irrational decisions 136 Italy 106–7 Ito’s Lemma 194 Japan 39, 43, 82–3, 92, 94, 98–9, 101, 106, 132, 142, 145–6, 157, 212, 217–25, 228, 269–70 Jensen, Michael 117 Jett, Joseph 143 JP Morgan (company) 72, 150, 152, 160, 162, 249–50, 268–9, 284–5, 299; see also Morgan Guaranty junk bonds 231, 279, 282, 291, 296–7 JWM Associates 175 Kahneman, Daniel 136 Kaplanis, Costas 174 Kassouf, Sheen 253 Kaufman, Henry 62 Kerkorian, Kirk 296 Keynes, J.M. 167, 175, 198 Keynesianism 5 Kidder Peabody 143 Kleinwort Benson 40 Korea 9, 226, 278 Kozeny, Viktor 121 Krasker, William 168 Kreiger, Andy 319 Kyoto Protocol 320 Lavin, Jack 102 law of large numbers 192 Leeson, Nick 51, 131, 143, 151 legal opinions 47, 219–20, 235, 273–4 Leibowitz, Martin 184 Leland, Hayne 42, 202 Lend Lease Corporation 261–2 leptokurtic conditions 163 leverage 31–2, 48–50, 54, 99, 102–3, 114, 131–2, 171–5, 213–14, 247, 270–3, 291, 295, 305, 308 Lewis, Kenneth 303 Lewis, Michael 77–8 life insurance 204–5 Lintner, John 111 liquidity options 175 liquidity risk 158, 173 litigation 297–8 Ljunggren, Bernt 38–40 London Inter-Bank Offered Rate (LIBOR) 6, 37 ‘long first coupon’ strategy 39 Long Term Capital Management (LTCM) 44, 51, 62, 77–8, 84, 114, 166–75, 187, 206, 210, 215–18, 263–4, 309–10 Long Term Credit Bank of Japan 94 LOR (company) 202 Louisiana Purchase 319 low exercise price options (LEPOs) 261 Maastricht Treaty and criteria 106–7 McLuhan, Marshall 134 McNamara, Robert 182 macro-economic indicators, derivatives linked to 319 Mahathir Mohammed 31 Malaysia 9 management consultants 72–3 Manchester United 152 mandatory convertibles 255 Marakanond, Rerngchai 302 margin calls 97–8, 175 ‘market neutral’ investment strategy 114 market risk 158, 173, 265 marketable eurodollar collateralized securities (MECS) 232 Markowitz, Harry 110 mark-to-market accounting 10, 100, 139–41, 145, 150, 174, 215–16, 228, 244, 266, 292, 295, 298 Marx, Groucho 24, 57, 67, 117, 308 13_INDEX.QXD 17/2/06 4:44 pm Page 331 Index mathematics applied to financial instruments 209–10; see also ‘quants’ matrix structures 72 Meckling, Herbert 117 Melamed, Leo 34, 211 merchant banks 38 Meriwether, John 167–9, 172–5 Merrill Lynch 124, 150, 217, 232 Merton, Robert 22, 42, 168–70, 175, 185, 189–90, 193–7, 210 Messier, Marie 247 Metallgesellschaft 95–7 Mexico 44 mezzanine finance 285–8, 291–7 MG Refining and Marketing 95–8, 114 Microsoft 53 Mill, Stuart 130 Miller, Merton 22, 101, 194 Milliken, Michael 282 Ministry of Finance, Japan 222 misogyny 75–7 mis-selling 238, 297–8 Mitchell, Edison 70 Mitchell & Butler 275–6 models financial 42–3, 141–2, 163–4, 173–5, 181–4, 189, 198–9, 205–10 of business processes 73–5 see also credit models Modest, David 168 momentum investment 111 monetization 260–1 monopolies in financial trading 124 moral hazard 151, 280, 291 Morgan Guaranty 37–8, 221, 232 Morgan Stanley 76, 150 mortgage-backed securities (MBSs) 282–3 Moscow, City of 277 moves of staff between firms 150, 244 Mozer, Paul 169 Mullins, David 168–70 multi-skilling 73 331 Mumbai 3 Murdoch, Rupert 247 Nabisco 220 Napoleon 113 NASDAQ index 64, 112 Nash, Ogden 306 National Australia Bank 144, 178 National Rifle Association 29 NatWest Bank 144–5, 198 Niederhoffer, Victor 130 ‘Nero’ 7, 31, 45–9, 60, 77, 82–3, 88–9, 110, 118–19, 125, 128, 292 NERVA 297 New Zealand 319 Newman, Frank 104 news, financial 133–4 News Corporation 247 Newton, Isaac 162, 210 Nippon Credit Bank 106, 271 Nixon, Richard 33 Nomura Securities 218 normal distribution 160–3, 193, 199 Northern Electric 248 O’Brien, John 202 Occam, William 188 off-balance sheet transactions 32–3, 99, 234, 273, 282 ‘offsites’ 74–5 oil prices 30, 33, 89–90, 95–7 ‘omitted variable’ bias 209–10 operational risk 158, 176 opinion shopping 47 options 9, 21–2, 25–6, 32, 42, 90, 98, 124, 197, 229 pricing 185, 189–98, 202 Orange County 16, 44, 50, 124–57, 212–17, 232–3 orphan subsidiaries 234 over-the-counter (OTC) market 26, 34, 53, 95, 124, 126 overvaluation 64 13_INDEX.QXD 17/2/06 4:44 pm Page 332 332 Index ‘overwhelming force’ strategy 134–5 Owen, Martin 145 ownership, ‘legal’ and ‘economic’ 247 parallel loans 35 pari-mutuel auction system 319 Parkinson’s Law 136 Parmalat 250, 298–9 Partnoy, Frank 87 pension funds 43, 108–10, 115, 204–5, 255 People’s Bank of China (PBOC) 276–7 Peters’ Principle 71 petrodollars 71 Pétrus (restaurant) 121 Philippines, the 9 phobophobia 177 Piga, Gustavo 106 PIMCO 19 Plaza Accord 38, 94, 99, 220 plutophobia 177 pollution quotas 320 ‘portable alpha’ strategy 115 portfolio insurance 112, 202–3, 294 power reverse dual currency (PRDC) bonds 226–30 PowerPoint 75 preferred exchangeable resettable listed shares (PERLS) 255 presentations of business models 75 to clients 57, 185 prime brokerage 309 Prince, Charles 238 privatization 205 privity of contract 273 Proctor & Gamble (P&G) 44, 101–4, 155, 298, 301 product disclosure statements (PDSs) 48–9 profit smoothing 140 ‘programme’ issuers 234–5 proprietary (‘prop’) trading 60, 62, 64, 130, 174, 254 publicly available information (PAI) 277 ‘puff’ effect 148 purchasing power parity theory 92 ‘put’ options 90, 131, 256 ‘quants’ 183–9, 198, 208, 294 Raabe, Matthew 217 Ramsay, Gordon 121 range notes 225 real estate 91, 219 regulatory arbitrage 33 reinsurance companies 288–9 ‘relative value’ trading 131, 170–1, 310 Reliance Insurance 91–2 repackaging (‘repack’) business 230–6, 282, 290 replication in option pricing 195–9, 202 dynamic 200 research provided to clients 58, 62–4, 184 reserves, use of 140 reset preference shares 254–7 restructuring of loans 279–81 retail equity products 258–9 reverse convertibles 258–9 reverse dual currency bonds 223–30 ‘revolver’ loans 284–5 risk, financial, types of 158 risk adjusted return on capital (RAROC) 268, 290 risk conservation principle 229–30 risk management 65, 153–79, 184, 187, 201, 267 risk models 163–4, 173–5 riskless portfolios 196–7 RJ Reynolds (company) 220–1 rogue traders 176, 313–16 Rosenfield, Eric 168 Ross, Stephen 196–7, 202 Roth, Don 38 Rothschild, Mayer Amshel 267 Royal Bank of Scotland 298 Rubinstein, Mark 42, 196–7 13_INDEX.QXD 17/2/06 4:44 pm Page 333 Index Rumsfeld, Donald 12, 134, 306 Rusnak, John 143 Russia 45, 80, 166, 172–3, 274, 302 sales staff 55–60, 64–5, 125, 129, 217 Salomon Brothers 20, 36, 54, 62, 167–9, 174, 184 Sandor, Richard 34 Sanford, Charles 72, 269 Sanford, Eugene 269 Schieffelin, Allison 76 Scholes, Myron 22, 42, 168–71, 175, 185, 189–90, 193–7, 263–4 Seagram Group 247 Securities and Exchange Commission, US 64, 304 Securities and Futures Authority, UK 249 securitization 282–90 ‘security design’ 254–7 self-regulation 155 sex discrimination 76 share options 250–1 Sharpe, William 111 short selling 30–1, 114 Singapore 9 single-tranche CDOs 293–4, 299 ‘Sisters of Perpetual Ecstasy’ 234 SITCOMs 313 Six Continents (6C) 275–6 ‘smile’ effect 145 ‘snake’ currency system 203 ‘softing’ arrangements 117 Solon 137 Soros, George 44, 130, 253, 318–19 South Sea Bubble 210 special purpose asset repackaging companies (SPARCs) 233 special purpose vehicles (SPVs) 231–4, 282–6, 290, 293 speculation 29–31, 42, 67, 87, 108, 130 ‘spinning’ 64 333 Spitzer, Eliot 64 spread 41, 103; see also credit spreads stack hedges 96 Stamenson, Michael 124–5 standard deviation 161, 193, 195, 199 Steinberg, Sol 91 stock market booms 258, 260 stock market crashes 42–3, 168, 203, 257, 259, 319 straddles or strangles 131 strategy in banking 70 stress testing 164–6 stripping of convertible bonds 253–4 structured investment products 44, 112, 115, 118, 128, 211–39, 298 structured note asset packages (SNAPs) 233 Stuart SC 18, 307, 316–18 Styblo Bleder, Tanya 153 Suharto, Thojib 81–2 Sumitomo Corporation 100, 142 Sun Tzu 61 Svensk Exportkredit (SEK) 38–9 swaps 5–10, 26, 35–40, 107, 188, 211; see also equity swaps ‘swaptions’ 205–6 Swiss Bank Corporation (SBC) 248–9 Swiss banks 108, 305 ‘Swiss cheese theory’ 176 synthetic securitization 284–5, 288–90 systemic risk 151 Takeover Panel 248–9 Taleb, Nassim 130, 136, 167 target redemption notes 225–6 tax and tax credits 171, 242–7, 260–3 Taylor, Frederick 98, 101 team-building exercises 76 team moves 149 technical analysis 60–1, 135 television programmes about money 53, 62–3 Thailand 9, 80, 302–5 13_INDEX.QXD 17/2/06 4:44 pm Page 334 334 Index Thatcher, Margaret 205 Thorp, Edward 253 tobashi trades 105–7 Tokyo Disneyland 92, 212 top managers 72–3 total return swaps 246–8, 269 tracking error 138 traders in financial products 59–65, 129–31, 135–6, 140, 148, 151, 168, 185–6, 198; see also dealers trading limits 42, 157, 201 trading rooms 53–4, 64, 68, 75–7, 184–7, 208 Trafalgar House 248 tranching 286–9, 292, 296 transparency 26, 117, 126, 129–30, 310 Treynor, Jack 111 trust investment enhanced return securities (TIERS) 216, 233 trust obligation participating securities (TOPS) 232 TXU Europe 279 UBS Global Asset Management 110, 150, 263–4, 274 uncertainty principle 122–3 unique selling propositions 118 unit trusts 109 university education 187 unspecified fund obligations (UFOs) 292 ‘upfronting’ of income 139, 151 Valéry, Paul 163 valuation 64, 142–6 value at risk (VAR) concept 160–7, 173 value investing 111 Vanguard 116 vanity bonds 230 variance 161 Vietnam War 182, 195 Virgin Islands 233–4 Vivendi 247–8 volatility of bond prices 197 of interest rates 144–5 of share prices 161–8, 172–5, 192–3, 199 Volcker, Paul 20, 33 ‘warehouses’ 40–2, 139 warrants arbitrage 99–101 weather, bonds linked to 212, 320 Weatherstone, Dennis 72, 268 Weil, Gotscal & Manges 298 Weill, Sandy 174 Westdeutsche Genosenschafts Zentralbank 143 Westminster Group 34–5 Westpac 261–2 Wheat, Allen 70, 72, 106, 167 Wojniflower, Albert 62 World Bank 4, 36, 38 World Food Programme 320 Worldcom 250, 298 Wriston, Walter 71 WTI (West Texas Intermediate) contracts 28–30 yield curves 103, 188–9, 213, 215 yield enhancement 112, 213, 269 ‘yield hogs’ 43 zaiteku 98–101, 104–5 zero coupon bonds 221–2, 257–8

This means you can also answer questions like, ‘What is the likely maximum price change at a specific probability level, say 99%, one in 100 days?’ VAR signifies the maximum amount that you could lose as a result of market price moves for a given probability over a fixed time. For example, a VAR figure of $50 million at a 99% 10 day holding period means that the bank has a 99% probability that it will not suffer a loss of more than $50 million over a ten-day period. This was a great advance in risk management. You might have thought that you could lose your shirt but you didn’t know by how much or how likely it was. VAR arose out of the work of JP Morgan to help its clients quantify risk. Today, VAR and its descendants are the ubiquitous means of measuring risk.


pages: 840 words: 202,245

Age of Greed: The Triumph of Finance and the Decline of America, 1970 to the Present by Jeff Madrick

Abraham Maslow, accounting loophole / creative accounting, Alan Greenspan, AOL-Time Warner, Asian financial crisis, bank run, Bear Stearns, book value, Bretton Woods, business cycle, capital controls, Carl Icahn, collapse of Lehman Brothers, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, desegregation, disintermediation, diversified portfolio, Donald Trump, financial deregulation, fixed income, floating exchange rates, Frederick Winslow Taylor, full employment, George Akerlof, Glass-Steagall Act, Greenspan put, Hyman Minsky, income inequality, index fund, inflation targeting, inventory management, invisible hand, John Bogle, John Meriwether, junk bonds, Kitchen Debate, laissez-faire capitalism, locking in a profit, Long Term Capital Management, low interest rates, market bubble, Mary Meeker, Michael Milken, minimum wage unemployment, MITM: man-in-the-middle, Money creation, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, Myron Scholes, new economy, Nixon triggered the end of the Bretton Woods system, North Sea oil, Northern Rock, oil shock, Paul Samuelson, Philip Mirowski, Phillips curve, price stability, quantitative easing, Ralph Nader, rent control, road to serfdom, Robert Bork, Robert Shiller, Ronald Coase, Ronald Reagan, Ronald Reagan: Tear down this wall, scientific management, shareholder value, short selling, Silicon Valley, Simon Kuznets, tail risk, Tax Reform Act of 1986, technology bubble, Telecommunications Act of 1996, The Chicago School, The Great Moderation, too big to fail, union organizing, V2 rocket, value at risk, Vanguard fund, War on Poverty, Washington Consensus, Y2K, Yom Kippur War

Morgan had developed a new measure, based on past price behavior, the volatility of securities, and the mix of securities held, that they argued could determine how likely losses on a portfolio were on any single day. They called it Value at Risk, or VAR. For example, VAR might find that a loss of 25 percent of a portfolio of assets would occur only once in twenty-five times. If the investment firm had more than enough capital to cover the maximum likely to be lost, according to VAR, it could feel comfortable borrowing still more to raise investment levels. For portfolio managers, VAR became invaluable. If VAR was too high, it could sell assets, or specifically, the more volatile assets. Diversifying assets was also thought to be a key way to reduce VAR, because different kinds of securities—say a California state bond and a Michigan state bond—rose or fell at different times; mixing securities usually meant less volatility overall.

., prl.1, prl.2, 7.1 Trump, Donald Tsai, Gerald Tudor Investment Tunney, John, 7.1, 7.2 Turner, Ed Turner, Ted, 8.1, 8.2, 8.3, 8.4, 8.5, 13.1 Turner Broadcasting Network (TNT), 8.1, 8.2, 8.3 Tuttle, Holmes Twentieth Century Fox, 7.1, 8.1, 8.2 Two Lucky People (Friedman and Friedman), 2.1 Tyco, 17.1, 17.2 Tynan, Kenneth UBS, 19.1, 19.2, 19.3, 19.4 Uhler, James Carvel, prl.1, prl.2 Uhler, Lewis, ix–x, prl.1, prl.2, 2.1, 7.1, 7.2 underwriters, 1.1, 6.1, 13.1, 13.2, 16.1, 16.2, 16.3, 17.1, 17.2, 17.3, 17.4, 17.5, 17.6, 17.7, 18.1, 19.1 unemployment insurance, 2.1, 2.2, 7.1 unemployment rate, prl.1, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 3.1, 3.2, 4.1, 6.1, 8.1, 8.2, 9.1, 9.2, 9.3, 9.4, 9.5, 10.1, 11.1, 11.2, 11.3, 11.4, 12.1, 12.2, 12.3, 14.1, 14.2, 14.3, 14.4, 14.5, 17.1, 19.1, 19.2, 19.3, 19.4, 19.5, 19.6 United Technologies, 4.1, 5.1 Unruh, Jesse Updike, John uranium, 4.1, 5.1, 14.1 Utah International, 4.1, 5.1, 5.2, 12.1, 12.2 Value at Risk (VAR), 15.1, 15.2, 15.3, 15.4, 15.5, 17.1, 17.2 Vanguard Funds Van Horn, Rob Versailles, Treaty of (1919) Veterans Administration (VA), 18.1, 18.2 Viacom, 8.1, 16.1 Vietnam War, prl.1, 1.1, 2.1, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 7.1, 10.1, 12.1, 19.1 Vilar, Alberto Viner, Jacob, 2.1, 2.2 Vinson & Elkins Volcker, Paul, 11.1, 11.2; background of, 3.1, 6.1, 11.3; in Carter administration, 11.4, 11.5, 11.6; as Federal Reserve chairman, itr.1, 6.2, 6.3, 6.4, 9.1, 9.2, 11.7, 13.1, 13.2, 13.3, 14.1, 15.1, 18.1, 19.1, 19.2; Greenspan compared with, 14.2, 14.3, 14.4, 14.5, 14.6; inflation policy of, 6.5, 6.6, 6.7, 6.8, 9.3, 11.8, 11.9, 11.10, 11.11, 11.12, 11.13, 11.14, 11.15, 11.16, 11.17, 14.7, 14.8; interest rates policy of, 6.9, 6.10, 9.4, 11.18, 11.19, 11.20, 11.21, 11.22, 15.2, 18.2, 18.3; in Reagan administration, 11.23, 11.24, 11.25; tax policy of, 11.26, 11.27, 11.28; as Treasury undersecretary, 3.2, 3.3, 6.11, 6.12, 6.13, 9.5, 9.6; unemployment rate and, 11.29, 11.30 Voorhis, Jerry Vranos, Michael, 12.1, 18.1 Wachtel, Paul wage controls, 2.1, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 14.1 wage levels, itr.1, prl.1, prl.2, prl.3, 1.1, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 2.10, 2.11, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6, 4.1, 4.2, 8.1, 8.2, 9.1, 9.2, 9.3, 10.1, 10.2, 11.1, 11.2, 11.3, 11.4, 11.5, 12.1, 12.2, 13.1, 14.1, 14.2, 14.3, 16.1, 17.1, 17.2, 19.1, 19.2 Walker, Charls Wall, Danny Wallace, George Wallich, Henry Wall Street, x, 1.1, 1.2, 1.3, 1.4, 3.1, 4.1, 6.1, 8.1, 8.2, 8.3, 9.1, 9.2, 9.3, 11.1, 12.1, 12.2, 12.3, 12.4, 13.1, 13.2, 13.3, 13.4, 13.5, 14.1, 14.2, 14.3, 14.4, 14.5, 15.1, 15.2, 15.3, 15.4, 15.5, 15.6, 16.1, 16.2, 16.3, 16.4, 16.5, 16.6, 17.1, 17.2, 18.1, 19.1, 19.2, 19.3, 19.4, 19.5, 19.6, 19.7, 19.8, 19.9, 19.10, 19.11, 19.12, 19.13, 19.14 Wall Street Journal, 2.1, 6.1, 16.1, 16.2, 17.1, 17.2 Wal-Mart, 8.1, 8.2 Walras, Léon Walters, Barbara Walton, Bud Walton, Sam, 8.1, 8.2, 12.1 Warner, Douglas, III Warner-Amex Cable, 8.1, 8.2 Warner Bros., 7.1, 8.1, 8.2 Warner Bros.

Diversifying assets was also thought to be a key way to reduce VAR, because different kinds of securities—say a California state bond and a Michigan state bond—rose or fell at different times; mixing securities usually meant less volatility overall. The managers could also buy hedges—offsetting investments—to reduce VAR. International regulators also placed their faith in VAR. The Bank for International Settlements (BIS) headquartered in Basel, Switzerland, in what were known as the Basel Agreements, set capital requirements for bankers according to the VAR of their portfolio of assets. Commercial banks, too, now trading actively for their portfolios, used VAR. The Meriwether group used VAR to calm concerns of Salomon management that they were leveraging too aggressively. If the quants had a sure way to measure the risk they were taking, they could justify borrowing still more.


pages: 290 words: 83,248

The Greed Merchants: How the Investment Banks Exploited the System by Philip Augar

Alan Greenspan, Andy Kessler, AOL-Time Warner, barriers to entry, Bear Stearns, Berlin Wall, Big bang: deregulation of the City of London, Bonfire of the Vanities, business cycle, buttonwood tree, buy and hold, capital asset pricing model, Carl Icahn, commoditize, corporate governance, corporate raider, crony capitalism, cross-subsidies, deal flow, equity risk premium, financial deregulation, financial engineering, financial innovation, fixed income, Glass-Steagall Act, Gordon Gekko, high net worth, information retrieval, interest rate derivative, invisible hand, John Meriwether, junk bonds, Long Term Capital Management, low interest rates, Martin Wolf, Michael Milken, new economy, Nick Leeson, offshore financial centre, pensions crisis, proprietary trading, regulatory arbitrage, risk free rate, Sand Hill Road, shareholder value, short selling, Silicon Valley, South Sea Bubble, statistical model, systematic bias, Telecommunications Act of 1996, The Chicago School, The Predators' Ball, The Wealth of Nations by Adam Smith, transaction costs, tulip mania, value at risk, yield curve

Computers make all this possible; no longer is risk assessed with a series of phone calls and a quick tally on a scratch pad.’11 At the same time as risk management procedures became more structured, value-at-risk (VAR) was adopted as the industry’s risk modelling tool. VAR is based on a paper called ‘Portfolio Selection’ published in the Journal of Finance in 1952 by Professor Harry Markowitz, which explored how investors could construct portfolios in order to optimize expected returns for a given level of risk. These techniques were taken up by asset managers, but it was not until 1993, when the Group of Thirty coined the term value-at-risk in a report on risk management for derivatives dealers, that they spread to investment banks.

Rothschild 31–2 narcissism, organizational 198 NASDAQ index 12, 13 National Association of Securities Dealers (NASD) 185, 188, 201 National City Bank 6–7 Neuer Markt 73–4 New York Stock Exchange 187–8, 201, 205 Office of Federal Home Loan Oversight 80 Office of Risk Assessment 205 O’Kelly, Gene 200–201 oligopoly 102 O’Neal, Stanley 23, 61, 135–6 Ong, Belita 11–12 options, definition of 77 output of investment banks, evaluation of 63–5, 85, 100 over-the-counter derivatives 77–8, 81, 89 Paine Webber 45 Parmalat 84, 161 Partnoy, Frank 41, 181–2, 209 Paulson, Henry 197, 206 Pecora, Judge Ferdinand 85 Perella, Joseph 43 perfect competition 102 Pitt, Harvey 187 Plender, John 208 PNC Financial 81–2 political connections 181–4, 209 prices 86–7, 97–8, 100–101, 172 advisory work 90–91, 94–5, 96–7 basis point pricing 91–3 collusion 93–8, 100–103 derivatives 88–9 and fund managers 192 negotiation, lack of 176–8, 179 under oligopoly 102 pre-announcement movements, shares 122 share trading 87–8, 89 strategic pricing 94–5, 98 underwriting 89–90, 92, 94–6 prime brokerage 133 private equity 132 privatization, UK 181 product development 131–4 product range 33 profits 51–2, 61–2 and compensation 60, 99–100 diverting attention from 95 falling, implications of 209–10 identification of 192–3 outlook for 209–10 return on equity (ROE) 54–7, 58 source of 166–7 programme trades 87, 88 proprietary trading 114–19, 192, 206, 211–12 prospect theory 180 Prudential-Bache Securities 11 Prudential plc 82 Public Company Accounting Oversight Board 200 Purcell, Philip 14, 22, 23–4, 39, 138, 205–6 Quattrone, Frank 19–20, 137–8, 151 Qwest 82 Racketeer-Influenced Corrupt Organizations law (RICO) 10, 26 recession, post-2001 13–14 Reed, John 189 regulation 7–8, 20, 81, 183, 185–90, 199 and bundling 193 integrated structure, failure to reform 22, 23, 24 and lobbying 209 recent 19, 200–203, 205, 209–10 regulatory arbitrage 185 relationship banking, demise of 35–6, 152–3, 170 research 66–7, 68, 69, 140–41, 145, 146 independent 69, 201–2, 204 internal position of 196 see also analysts Restoring Trust: Investment in the Twenty-First Century 194 returns 49–51, 61–2, 99–100, 165–6 and analysts’ recommendations 67 compensation 58–60, 99–100, 165–6 and cost of equity capital 57–8 excess, source of 166–7 falling, implications of 209–10 margins 52–3, 88–9, 119 outlook for 209–10 profits 51–2 return on equity (ROE) 54–7, 58, 61 risk management 111–13, 125–31 risk premium 55, 56 Ritter, Professor Jay 71, 89–90, 94–5, 179 Rogers, John 68 Roosevelt, President Franklin D. 7 Roye, Paul 191 Rubin, Robert 42, 182–3 salaries see compensation Salomon Brothers 11, 34, 37, 41–2, 137, 148–9 cynicism, culture of 152, 153 losses, 1994 128 and WorldCom 121 Salomon Smith Barney 17, 41, 150–51, 195 Sanford Bernstein 32 Sants, Hector 170–71, 189 Sarbanes-Oxley Act (2002) 19, 200–201, 209 Sassoon, James 184 Saunders, Ernest 11 scandals see corruption/malpractice Schapiro, Mary 188 Schroders 42 Securities and Exchange Commission (SEC) 7, 19, 21, 23, 185–7, 205 Securities Industry Association 183 securitization 78 securitized bonds 46 settlements paid by investment banks 19, 20, 23, 24, 38, 39, 42, 43, 201, 207 share prices, pre-announcement movements 122 shareholder activism 203–4 shareholder value 8–9, 63, 169 shareholders, and corporate control 163–4, 203–4 Sherman Anti-Trust Act (1890) 5–6, 8 Smith, Adam 86, 171–2 Smith, David 171 Smith, Professor Roy 195 Smith Barney 40, 41 Southern Peru Copper Corp 91 special purpose entities/vehicles (SPEs/SPVs) 78, 82, 83 ‘specialists’ 115–16 ‘spinning’ 17–18, 43, 137, 160 Spitzer, Eliot 15, 65–6 inquiry headed by 15, 16–17, 21, 22, 24, 38, 200 whispering campaign against 23 status of investment banking 4–5, 12, 18 Stevenson, Lord 175 Stiglitz, Joseph 12, 64, 176, 182–3 stock exchanges, structural reform 212 stock markets bull market mentality 3–5, 12–13, 64, 65, 71–3 performance of 63–4 public interest in 12 stock options 176 new accounting rules 201, 209 Stonehill, Charles 147–8 structural reform 211–15 Sudarsanam, Professor Sudi 76 Summers, Lawrence 63 swaps 77, 132 Sykes, Sir Richard 210 taxation, 401K amendment 9 team ethos 124–5 Thain, John 188, 209 Time Warner 13, 14, 76–7 Tomlinson, Lindsay 194 transaction banking 152–3, 170 transaction costs 67 Travelers 41 treasurers (company), and derivatives 80 Treasury bond scandal 41–2 ‘Triple Play’ 121 Truman, President Harry S. 7 trustees, mutual/pension funds 191–2 UBS 31, 32, 37 underwriting fees 89–90, 92, 94–6 value-at-risk (VAR) 117, 129–31 volatility of markets, and performance of investment banks 24–5, 51–2, 57–8 Wall Street 9 Wall Street Crash 6–7 Walter, Professor Ingo 195 Wasserman, Ed 18 Wasserstein, Bruce 31, 43, 77 Weill, Sanford 195 Welch, Jack 45 Wertheim 147 Wheat, Allen 59, 137 WorldCom 17, 121, 150–51, 161 Zumwinkel, Klaus 178–9

The investment banks disclose value-at-risk data, which gives some clue as to the scale of activities; but everyone’s model is slightly different and it is not possible to estimate what profits the stated value-at-risk might generate. The average daily value-at-risk of the large investment banks varied in 2003 from, for example, $15m at Bear Stearns to $58m at Goldman Sachs. As Goldman Sachs said, ‘This means that there is a 1 in 20 chance that daily trading net revenues will fall below the expected daily trading net revenue by an amount at least as large as the reported value-at-risk.’21 Even with annual trading revenues of over $10 billion, this is a far from negligible risk but there is some protection from shareholders’ equity, which is several hundred times value-at-risk – and of course from the Edge, which substantially reduces the real risk.


pages: 543 words: 153,550

Model Thinker: What You Need to Know to Make Data Work for You by Scott E. Page

Airbnb, Albert Einstein, Alfred Russel Wallace, algorithmic trading, Alvin Roth, assortative mating, behavioural economics, Bernie Madoff, bitcoin, Black Swan, blockchain, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Checklist Manifesto, computer age, corporate governance, correlation does not imply causation, cuban missile crisis, data science, deep learning, deliberate practice, discrete time, distributed ledger, Easter island, en.wikipedia.org, Estimating the Reproducibility of Psychological Science, Everything should be made as simple as possible, experimental economics, first-price auction, Flash crash, Ford Model T, Geoffrey West, Santa Fe Institute, germ theory of disease, Gini coefficient, Higgs boson, High speed trading, impulse control, income inequality, Isaac Newton, John von Neumann, Kenneth Rogoff, knowledge economy, knowledge worker, Long Term Capital Management, loss aversion, low skilled workers, Mark Zuckerberg, market design, meta-analysis, money market fund, multi-armed bandit, Nash equilibrium, natural language processing, Network effects, opioid epidemic / opioid crisis, p-value, Pareto efficiency, pattern recognition, Paul Erdős, Paul Samuelson, phenotype, Phillips curve, power law, pre–internet, prisoner's dilemma, race to the bottom, random walk, randomized controlled trial, Richard Feynman, Richard Thaler, Robert Solow, school choice, scientific management, sealed-bid auction, second-price auction, selection bias, six sigma, social graph, spectrum auction, statistical model, Stephen Hawking, Supply of New York City Cabdrivers, systems thinking, tacit knowledge, The Bell Curve by Richard Herrnstein and Charles Murray, The Great Moderation, the long tail, The Rise and Fall of American Growth, the rule of 72, the scientific method, The Spirit Level, the strength of weak ties, The Wisdom of Crowds, Thomas Malthus, Thorstein Veblen, Tragedy of the Commons, urban sprawl, value at risk, web application, winner-take-all economy, zero-sum game

While the Polya process reveals the core idea that interactions produce path dependence, we need more realistic models for that insight to guide action. Value at Risk and Volatility We can interpret the standard deviation in a time series of data as volatility. Investments in stocks, real estate, and privately held businesses all exhibit volatility. Value at risk (VaR) measures the probability of a loss of a given amount during a specific time period. An investment with a one-year 5% VaR of $10,000 has a 5% probability of losing more than $10,000 at the end of one year.10 Banks use VaR calculations to determine the amount of assets that must be kept on hand to avoid bankruptcy.

total value, 108 Tractatus Logico-Philosophicus (Wittgenstein), 6 training sets, 87 transition probabilities, 190, 191, 351 transition rule, 182 transition-to-addiction model, 341 transitivity, in rational-actor model, 49 TROLL, 260 Troubled Asset Relief Program (TARP), 21–22 truth-telling, 285 TurboTax, 76 Tversky, Amos, 51–52 two-dimensional median, 233 typhoid, 140 uncertainty, 144 unconditional generosity, 303 uniform distribution, 149, 150 uniqueness, rational choice and, 50 United States Air Force, 10–11 utility functions, 48–49 vaccination threshold, 138 valence attributes, in hedonic competition model, 237 valuation error, defining, 35 value, 332 last-on-the-bus, 108, 115 many-model thinking for, 241–242 of signals, 301–302 value at risk (VaR), 170 value function, 108 VaR. See value at risk variables dependent, 84 independent, 84 multiple-variable regression, 88–89 multivariable linear models, 87–90 omitted, 84–85 variance in normal distribution, 60–61 percentage of, 34 veto players, 234–236 Vinlanders, 270 volatility long-tailed distributions and, 77–78 VaR and, 170 voluntary participation, 285 von Neumann, John, 95 Voronoi neighborhoods, 231 spatial competition model with, 231 (fig.)

For example, to secure an investment with a two-week 40% VaR of $100,000, an investor may be asked to hold $100,000 in cash. If an investment follows a simple random walk with an increase or decrease of size M each period, then it has an N period 2.5% VaR of .11 Thus, an investment that randomly goes up or down $1,000 each day has a nine-day 2.5% VaR of $6,000, and a one-year 2.5% VaR of $38,000. Notice that VaR increases linearly in the size of the steps but that it increases like the square root of the number of periods. We can use the formula for VAR to explain why the FDIC only requires that banks hold around 2% of their assets in cash overnight, but banks require that consumers put down 20% deposits on houses.


pages: 354 words: 105,322

The Road to Ruin: The Global Elites' Secret Plan for the Next Financial Crisis by James Rickards

"World Economic Forum" Davos, Affordable Care Act / Obamacare, Alan Greenspan, Albert Einstein, asset allocation, asset-backed security, bank run, banking crisis, barriers to entry, Bayesian statistics, Bear Stearns, behavioural economics, Ben Bernanke: helicopter money, Benoit Mandelbrot, Berlin Wall, Bernie Sanders, Big bang: deregulation of the City of London, bitcoin, Black Monday: stock market crash in 1987, Black Swan, blockchain, Boeing 747, Bonfire of the Vanities, Bretton Woods, Brexit referendum, British Empire, business cycle, butterfly effect, buy and hold, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, cellular automata, cognitive bias, cognitive dissonance, complexity theory, Corn Laws, corporate governance, creative destruction, Credit Default Swap, cuban missile crisis, currency manipulation / currency intervention, currency peg, currency risk, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, debt deflation, Deng Xiaoping, disintermediation, distributed ledger, diversification, diversified portfolio, driverless car, Edward Lorenz: Chaos theory, Eugene Fama: efficient market hypothesis, failed state, Fall of the Berlin Wall, fiat currency, financial repression, fixed income, Flash crash, floating exchange rates, forward guidance, Fractional reserve banking, G4S, George Akerlof, Glass-Steagall Act, global macro, global reserve currency, high net worth, Hyman Minsky, income inequality, information asymmetry, interest rate swap, Isaac Newton, jitney, John Meriwether, John von Neumann, Joseph Schumpeter, junk bonds, Kenneth Rogoff, labor-force participation, large denomination, liquidity trap, Long Term Capital Management, low interest rates, machine readable, mandelbrot fractal, margin call, market bubble, Mexican peso crisis / tequila crisis, Minsky moment, Money creation, money market fund, mutually assured destruction, Myron Scholes, Naomi Klein, nuclear winter, obamacare, offshore financial centre, operational security, Paul Samuelson, Peace of Westphalia, Phillips curve, Pierre-Simon Laplace, plutocrats, prediction markets, price anchoring, price stability, proprietary trading, public intellectual, quantitative easing, RAND corporation, random walk, reserve currency, RFID, risk free rate, risk-adjusted returns, Robert Solow, Ronald Reagan, Savings and loan crisis, Silicon Valley, sovereign wealth fund, special drawing rights, stock buybacks, stocks for the long run, tech billionaire, The Bell Curve by Richard Herrnstein and Charles Murray, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transfer pricing, value at risk, Washington Consensus, We are all Keynesians now, Westphalian system

Two days later, on October 2, The Washington Post published my op-ed “A Mountain, Overlooked: How Risk Models Failed Wall St. and Washington.” This was my first public effort to use complexity theory to explain the ongoing financial collapse. In the op-ed I wrote: Since the 1990s, risk management on Wall Street has been dominated by a model called “value at risk” (VaR). VaR attributes risk factors to every security and aggregates these factors across an entire portfolio, identifying those risks that cancel out. What’s left is “net” risk that is then considered in light of historical patterns. The model predicts with 99 percent probability that institutions cannot lose more than a certain amount of money.

Prevailing theory does even more damage when weighing the statistical properties of risk. The extended balance sheet of too-big-to-fail banks today is approximately one quadrillion dollars, or one thousand trillion dollars poised on a thin sliver of capital. How is the risk embedded in this leverage being managed? The prevailing theory is called value at risk, or VaR. This theory assumes that risk in long and short positions is netted, the degree distribution of price movements is normal, extreme events are exceedingly rare, and derivatives can be properly priced using a “risk-free” rate. In fact, when AIG was on the brink of default in 2008, no counterparty cared about its net position; AIG was about to default on the gross position to each counterparty.

The market risk in Goldman’s position boils down to the spread between the fixed rate Goldman pays and the fixed rate it receives. The spread between two-year notes and five-year notes is historically low. As a result, Goldman is required to hold very little capital against this risk. Wall Street banks use a formula called value at risk, or VaR, mentioned earlier, which implies Goldman has almost no risk. Under accounting and regulatory rules applied to swaps, the notes disappear, the accounting disappears, and almost all market risk disappears. It’s all good. Yet it’s not all good. In the real world, when Citibank and Bank of America do these trades with Goldman, they turn around and do trades in the opposite direction to hedge the risk to Goldman.


pages: 250 words: 79,360

Escape From Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do About It by Erica Thompson

Alan Greenspan, Bayesian statistics, behavioural economics, Big Tech, Black Swan, butterfly effect, carbon tax, coronavirus, correlation does not imply causation, COVID-19, data is the new oil, data science, decarbonisation, DeepMind, Donald Trump, Drosophila, Emanuel Derman, Financial Modelers Manifesto, fudge factor, germ theory of disease, global pandemic, hindcast, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, implied volatility, Intergovernmental Panel on Climate Change (IPCC), John von Neumann, junk bonds, Kim Stanley Robinson, lockdown, Long Term Capital Management, moral hazard, mouse model, Myron Scholes, Nate Silver, Neal Stephenson, negative emissions, paperclip maximiser, precautionary principle, RAND corporation, random walk, risk tolerance, selection bias, self-driving car, social distancing, Stanford marshmallow experiment, statistical model, systematic bias, tacit knowledge, tail risk, TED Talk, The Great Moderation, The Great Resignation, the scientific method, too big to fail, trolley problem, value at risk, volatility smile, Y2K

Risk management in finance often uses a mathematical construction called Value at Risk (VaR). The VaR can be defined in a few different ways, but in essence it quantifies the largest loss that might occur on a small fraction of occasions, generated from a model based on past data. Then we expect that 99% of the time the loss will not be larger than the VaR. There are a few problems with this simple description. First, the assumption that the past will be a reliable guide to the future is one that has been repeatedly shown to be incorrect. More sophisticated VaR models may price in either expectations about future changes (such as an increase in volatility) or more simply add a conservative margin for error.

More sophisticated VaR models may price in either expectations about future changes (such as an increase in volatility) or more simply add a conservative margin for error. Second, the VaR has nothing to say about the shape or magnitude of the tail risk itself. The 99th percentile is a useful boundary for what might happen on the worst of the good days, but if a bad day happens, you’re on your own. David Einhorn, manager of another hedge fund, described this as ‘like an air bag that works all the time, except when you have a car accident’. As such, using the VaR as a risk measure effectively encourages the concentration of risks into the tail above the reported quantile. If you are required to report a 99% VaR, you could, for example, bet on a coin toss not coming up heads seven times in a row: you would win 99.2% of the time and the VaR would be zero, no matter how large your bet.

Most of the time, it is reasonable and justifiable to model these kinds of events as being uncorrelated, but just occasionally something happens that ties them together, and this kind of large event is where insurers are vulnerable. The EU Directive called Solvency II is somewhat like the Basel banking requirements in that it specifies how insurers should calculate levels of capital to hold in preparation for possible large events. It uses something a bit like the VaR approach, requiring insurers to calculate a 1-in-200-year event (99.5% VaR). Doing that requires a model that can be calibrated on past data but will again have to make assumptions about how closely the future can be expected to resemble the past. A 1-in-200-year flood loss event, for example, is difficult to calculate for various reasons.


pages: 576 words: 105,655

Austerity: The History of a Dangerous Idea by Mark Blyth

"there is no alternative" (TINA), accounting loophole / creative accounting, Alan Greenspan, balance sheet recession, bank run, banking crisis, Bear Stearns, Black Swan, book value, Bretton Woods, business cycle, buy and hold, capital controls, Carmen Reinhart, Celtic Tiger, central bank independence, centre right, collateralized debt obligation, correlation does not imply causation, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, debt deflation, deindustrialization, disintermediation, diversification, en.wikipedia.org, ending welfare as we know it, Eugene Fama: efficient market hypothesis, eurozone crisis, financial engineering, financial repression, fixed income, floating exchange rates, Fractional reserve banking, full employment, German hyperinflation, Gini coefficient, global reserve currency, Greenspan put, Growth in a Time of Debt, high-speed rail, Hyman Minsky, income inequality, information asymmetry, interest rate swap, invisible hand, Irish property bubble, Joseph Schumpeter, Kenneth Rogoff, liberal capitalism, liquidationism / Banker’s doctrine / the Treasury view, Long Term Capital Management, low interest rates, market bubble, market clearing, Martin Wolf, Minsky moment, money market fund, moral hazard, mortgage debt, mortgage tax deduction, Occupy movement, offshore financial centre, paradox of thrift, Philip Mirowski, Phillips curve, Post-Keynesian economics, price stability, quantitative easing, rent-seeking, reserve currency, road to serfdom, Robert Solow, savings glut, short selling, structural adjustment programs, tail risk, The Great Moderation, The Myth of the Rational Market, The Wealth of Nations by Adam Smith, Tobin tax, too big to fail, Two Sigma, unorthodox policies, value at risk, Washington Consensus, zero-sum game

Figure 2.1 The “Normal” Distribution of probable events Change the variable from height to default probability, and you can see how such a way of thinking about the likelihood of future events could be of great use to banks as they tried to risk-adjust their portfolios and positions. The piece of technology that allowed banks to do this is known as Value at Risk (VaR) analysis, which is part of a larger class of mathematical models designed to help banks manage risk. What VaR does is generate a figure (a VaR number) for how much a firm can win or lose on an individual trade. By summing VaR numbers, one can estimate a firm’s total exposure. Consider the following example. What was the worst that could have happened to the US housing market in 2008? As in the height example, the answer depends on a data sample that calibrates the model.

., 168 Sweden as a welfare state, 214 austerity in, 17, 178–180, 191–193, 204, 206 economic recovery in the 1930s in, 126 expansionary contraction in, 209–210, 211 fiscal adjustment in, 173 Swedish Social Democrats, (SAP), 191 thirty-year bond in, 210–211 systemic risk, 44 Tabellini, Guido “Positive Theory of Fiscal Deficits and Government Debt in a Democracy, A”, 167 tail risk, 44 See also systemic risk Takahashi, Korekiyo, 199 Taleb, Nassim Nicolas, 32, 33, 34 “Tales of Fiscal Adjustment” (Alesina and Ardanga), 171, 205, 208, 209 Target Two payments, 91 Taylor, Alan, 73 Tax Justice Network, 244 Thatcher, Margaret, 15 “there is no alternative”, 98, 171–173, 175, 231 ThyssenKrupp, 132 Tilford, Simon, 83 “too big to bail,” 49, 51–93, 74, 82 European banks as, 83, 90, 92 “too big to fail”, 6, 16, 45, 47–50, 82, 231 Tooze, Adam, 196 Trichet, Jean Claude, 60 as president of the ECB, 176 on Greece and Ireland, 235 See also European Central Bank United Kingdom, 1 and the gold standard, 185, 189–191 asset footprint of top banks, 83 austerity in, 17, 122–125, 126, 178–180 and the global economy in the 1920s and 1930s, 184–189, 189–121 banking crisis in, 52 cost of, 45 depression in, 204 Eurozone Ten-Year Government Bond Yields, 80 fig. 3.2 Gordon Brown economics policy, 5, 59 housing bubble, 66–67 Lawson boom, 208 “Memoranda on Certain Proposals Relating to Unemployment” (UK White Paper), 122, 124 New Liberalism, 117–119 “Treasury view”, 101, 163–165 war debts to the United States, 185 United States, 2 AAA credit rating, 1, 2–3 Agricultural Adjustment Act, 188 and current economic conditions, 213 and printing its own money, 11 and recycling foreign savings, 11–12 and the Austrian School of economics, 121, 143–145, 148–152 and the gold standard, 188 assets of large banks in, 6 austerity in, 17, 119–122, 178–180, 187–189 “Banker’s doctrine”, 101 banking system of, 6 cost of crisis, 45, 52 Bush administration economics policy, 5, 58–59 capital-flow cycle in, 11 Congressional Research Service (CRS), 213 debt-ceiling agreement, 3 depression in, 188 Federal Reserve, 6, 157 federal taxes, 242–243 liberalism in, 119–122 liquidationism in, 119–122, 204 National Industrial Recovery Act, 188 repo market, 15–16, 24–25 rise in real estate prices in, 27 Securities and Exchange Commission, 49 Simson-Bowles Commission, 122, 122–125 Social Security Act, 188 stimulus in, 55 stop in capital flow in 1929, 190 Treasury bills, 25 Troubled Asset Relief Program, 58–59, 230 and American politics involvement, 59 Wagner’s Act, 188 Wall Street Crash of 1929, 204, 238 Washington Consensus, 142, 161–162, 165 Value at Risk (VaR) analysis, 34–38 Vienna agreement, 221 Viniar, David, 32 Wade, Robert, 13 Wagner, Richard, 156 Wartin, Christian, 137 Watson Institute for International Studies, ix “We Can Conquer Unemployment” (Lloyd George), 123, 24 “Wealth of Nations, The”, 109, 112 welfare, xi, 58 welfare state foundation for, 117 Wells Fargo, 48 Whyte, Philip, 83 Williamson, John, 161, 162 World Bank, 163, 210, 211 World Economic Outlook, 212

Indeed, the probability that all your mortgage bonds will go bad or that a very large bank will go bust is absurdly small, ten sigma or more, again, so long as you think that the probability distribution you face is normally distributed. Your VaR number, once calculated, would reflect this. Nassim Taleb never bought into this line of thinking. He had been a critic of VaR models as far back as 1997, arguing that they systematically underestimated the probability of high-impact, low-probability events. He argued that the thin tails of the Gaussian worked for height but not for finance, where the tails were “fat.”


pages: 311 words: 99,699

Fool's Gold: How the Bold Dream of a Small Tribe at J.P. Morgan Was Corrupted by Wall Street Greed and Unleashed a Catastrophe by Gillian Tett

"World Economic Forum" Davos, accounting loophole / creative accounting, Alan Greenspan, asset-backed security, bank run, banking crisis, Bear Stearns, Black-Scholes formula, Blythe Masters, book value, break the buck, Bretton Woods, business climate, business cycle, buy and hold, collateralized debt obligation, commoditize, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, easy for humans, difficult for computers, financial engineering, financial innovation, fixed income, Glass-Steagall Act, housing crisis, interest rate derivative, interest rate swap, inverted yield curve, junk bonds, Kickstarter, locking in a profit, Long Term Capital Management, low interest rates, McMansion, Michael Milken, money market fund, mortgage debt, North Sea oil, Northern Rock, Plato's cave, proprietary trading, Renaissance Technologies, risk free rate, risk tolerance, Robert Shiller, Satyajit Das, Savings and loan crisis, short selling, sovereign wealth fund, statistical model, tail risk, The Great Moderation, too big to fail, value at risk, yield curve

Like a hunter-gatherer tribe, all derivatives traders had an equal interest in upholding the norms. That was why any recommendation the G30 report might make about legislation to institute regulation was to be fought, argued Brickell, tooth and nail. Another key factor that influenced how J.P. Morgan bankers and others viewed regulation was the development of an idea known as value at risk, or VaR. In previous decades, banks had taken an ad hoc attitude toward measuring risk. They extended loans to customers they liked, withheld them from those they did not, and tried to prevent their traders from engaging in any market activity that looked too risky, but without trying to quantify those dangers with precision.

Weatherstone decided he wanted more, and he asked a team of quantitative experts to develop a technique that could measure how much money the bank stood to lose each day if the markets turned sour. It was the first time that any bank had ever done that, with the notable exception of Bankers Trust. For several months the so-called quants played around with ideas until they coalesced around the concept of value at risk (VaR). They decided that the goal should be to work out how much money the bank could expect to lose, with a probability of 95 percent, on any given day. The 95 percent was an accommodation to the hard reality that there would always be some risk in the markets that the models wouldn’t be able to account for.

Nor could risk be reduced to a few mathematical models. Fifteen years earlier, when Dennis Weatherstone ran the bank, J.P. Morgan had invented the concept of VaR and then disseminated it to the rest of the industry. It was a notable legacy. However, Dimon had no intention of giving undue veneration to VaR. Dimon (like Weatherstone) deemed mathematical models to be useful tools, but only when they were treated as a compass, not an oracle. Models could not do your thinking for you. The only safe way to use VaR, or so Dimon believed, was alongside numerous other analytical tools—including the human brain. By late 2004, speculation that Dimon was about to oust Harrison was rampant.


pages: 1,073 words: 302,361

Money and Power: How Goldman Sachs Came to Rule the World by William D. Cohan

"Friedman doctrine" OR "shareholder theory", "RICO laws" OR "Racketeer Influenced and Corrupt Organizations", Alan Greenspan, asset-backed security, Bear Stearns, Bernie Madoff, Bob Litterman, book value, business cycle, buttonwood tree, buy and hold, collateralized debt obligation, Cornelius Vanderbilt, corporate governance, corporate raider, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, deal flow, diversified portfolio, do well by doing good, fear of failure, financial engineering, financial innovation, fixed income, Ford paid five dollars a day, Glass-Steagall Act, Goldman Sachs: Vampire Squid, Gordon Gekko, high net worth, hiring and firing, hive mind, Hyman Minsky, interest rate swap, John Meriwether, junk bonds, Kenneth Arrow, London Interbank Offered Rate, Long Term Capital Management, managed futures, margin call, market bubble, mega-rich, merger arbitrage, Michael Milken, moral hazard, mortgage debt, Myron Scholes, paper trading, passive investing, Paul Samuelson, Ponzi scheme, price stability, profit maximization, proprietary trading, risk tolerance, Ronald Reagan, Saturday Night Live, short squeeze, South Sea Bubble, tail risk, time value of money, too big to fail, traveling salesman, two and twenty, value at risk, work culture , yield curve, Yogi Berra, zero-sum game

Later that night, Sparks responded to Winkelried that there was “[b]ad news everywhere” including that NovaStar, a subprime mortgage originator, announced bad earnings and lost one-third of its market value in one day and that Wells Fargo had fired more than three hundred people from its subprime mortgage origination business. But, he was happy to report, Goldman was “net short, but mostly in single name CDS and some tranched index vs the s[a]me index longs. We are working to cover more, but liquidity makes it tough. Volatility is causing our VAR [value at risk] numbers to grow dramatically,” which soon enough would make Goldman’s top brass concerned about the level of the firm’s capital being committed to these trades. Not surprisingly, in the midst of all of this intellectual and financial jousting in the market, Goldman’s senior executives occasionally wavered from the clear message that Viniar delivered in December 2006.

by Goldman Sachs, see Goldman Sachs, deals and underwriting of Medina’s judicial ruling on on mortgages risks of Union Investment Management United Aircraft United Cigar Manufacturers’ Corporation United Corporation United Technologies University Hill Foundation Univis Lens Co. Unocal, 11.1, 11.2, 11.3, 11.4, 11.5 UPI USG Corp. U.S. Steel utility bonds, 1.1, 5.1, 5.2 Utley, Kristine value-at-risk (VAR) system, 14.1, 20.1, 20.2, 22.1, 22.2, 22.3, 22.4, 23.1 Vanderbilt, Cornelius Vanity Fair, 17.1, 23.1, 24.1 van Praag, Lucas, 17.1, 17.2, 22.1, 22.2 Venice Victor, Ed Vietnam War Viniar, David, prl.1, 3.1, 18.1, 18.2, 19.1, 19.2, 19.3, 20.1, 20.2, 21.1, 21.2, 21.3, 21.4, 21.5, 21.6, 22.1, 22.2, 22.3, 22.4, 22.5, 22.6, 22.7, 23.1, 24.1 Senate testimony of, prl.1, prl.2, prl.3 Vogel, Jack, 7.1, 7.2, 7.3, 7.4 Vogel, Matthew Vogelstein, John Volcker, Paul, 13.1, 18.1, 18.2, 19.1 Voltaire Vranos, Michael Wachovia, financial troubles of Wachtell, Lipton, Rosen & Katz, 11.1, 11.2, 11.3 Waldorf-Astoria Hotel, 4.1, 7.1 Walgreens Walker, Doak Wall Street Journal, 5.1, 7.1, 7.2, 7.3, 7.4, 7.5, 8.1, 10.1, 12.1, 15.1, 16.1, 16.2, 17.1, 17.2, 18.1, 19.1, 20.1, 20.2, 20.3, 22.1, 23.1, 23.2, 23.3 article on M&A business, 9.1, 9.2, 9.3 on insider training scandal, 11.1, 11.2, 11.3, 11.4 Wall Street Letter, 12.1 Walt Disney Company, 7.1, 11.1, 17.1 Wambold, Ali Warburg Pincus Warner, Ernestine Warner, Douglas “Sandy,” 16.1, 16.2, 16.3 Warner Bros.

Birnbaum replied that he had a meeting with them on Tuesday, where apparently he was able to get the VAR limit of $110 million extended until August 21. But, on August 13, when VAR for trading overall had increased to $159 million, from $150 million, Viniar was explicit. “No comment necessary,” he wrote. “Get it down.” Gary Cohn echoed Viniar’s comment two days later, after the trading VAR had increased to $165 million. “There is no room for debate,” he wrote. “We must get down now.” The concern about the rising VAR on the mortgage trading desk revealed a larger debate then percolating around Goldman: how to take advantage of the misery being felt by other firms as the mortgage markets started to collapse.


pages: 475 words: 155,554

The Default Line: The Inside Story of People, Banks and Entire Nations on the Edge by Faisal Islam

"World Economic Forum" Davos, Alan Greenspan, Asian financial crisis, asset-backed security, balance sheet recession, bank run, banking crisis, Basel III, Ben Bernanke: helicopter money, Berlin Wall, Big bang: deregulation of the City of London, bond market vigilante , book value, Boris Johnson, British Empire, capital controls, carbon credits, carbon footprint, carbon tax, Celtic Tiger, central bank independence, centre right, collapse of Lehman Brothers, credit crunch, Credit Default Swap, crony capitalism, Crossrail, currency risk, dark matter, deindustrialization, Deng Xiaoping, disintermediation, energy security, Eugene Fama: efficient market hypothesis, eurozone crisis, Eyjafjallajökull, financial deregulation, financial engineering, financial innovation, financial repression, floating exchange rates, forensic accounting, forward guidance, full employment, G4S, ghettoisation, global rebalancing, global reserve currency, high-speed rail, hiring and firing, inflation targeting, Irish property bubble, junk bonds, Just-in-time delivery, labour market flexibility, light touch regulation, London Whale, Long Term Capital Management, low interest rates, margin call, market clearing, megacity, megaproject, Mikhail Gorbachev, mini-job, mittelstand, Money creation, moral hazard, mortgage debt, mortgage tax deduction, mutually assured destruction, Myron Scholes, negative equity, North Sea oil, Northern Rock, offshore financial centre, open economy, paradox of thrift, Pearl River Delta, pension reform, price mechanism, price stability, profit motive, quantitative easing, quantitative trading / quantitative finance, race to the bottom, regulatory arbitrage, reserve currency, reshoring, Right to Buy, rising living standards, Ronald Reagan, savings glut, shareholder value, sovereign wealth fund, tail risk, The Chicago School, the payments system, too big to fail, trade route, transaction costs, two tier labour market, unorthodox policies, uranium enrichment, urban planning, value at risk, WikiLeaks, working-age population, zero-sum game

Failing to recognise those tail events – being fooled by randomness – risks catastrophic policy error.’ But – no surprise – normal distributions are hard-wired into economics and quantitative financial modelling. The towering example of this is value-at-risk (VaR), the measure used by banks and regulators to assess risk on their trading books, and to set limits on traders. VaR is supposed to tell a bank and its regulators how much a trading portfolio will make on 99 per cent or sometimes 95 per cent of trading days. Remember that at the time Northern Rock and its competitors were going crazy, credit risk was migrating off balance sheets and out of the regulated loan book and into the trading book.

But global regulators demanded banks calculate, and sure enough the familiar pattern of misinterpreting, gaming and reverse engineering formulae was quickly applied to VaR. The Financial Times quoted Goldman Sachs’ chief financial officer during the 2007 credit crunch as saying that twenty-five standard deviation moves were happening several days in a row. To put that in context, he was suggesting that occurrences that his financial model suggested would only happen once in a period of many trillions of lifetimes of the universe, were actually happening every day. The ‘fatal flaw’ of VaR, as Haldane argues, is that it is silent about the tail risk. A trader could be given a so-called 99 per cent VaR limit of $10 million, but VaR would be blind to the trader’s construction of a portfolio that gave a 1 per cent chance of a $1 billion loss.

A basic problem is that past trading performance is no guide to the future. VaR models were routinely specified to assume that the very recent past is the best guide to the future. Before long VaR came to be seen, quite incorrectly, as an upper-end assessment of likely losses. In fact, the VaR measure essentially slices off the tails full of catastrophic risk. So it is useless as a means of managing catastrophe risk. It is a little like having an emergency parachute that opens swiftly – except when you jump out of a plane. VaR was never designed for this. In the wake of the crisis, central bankers around the world have responded to a campaign against Gaussian thin tails by the former-trader-turned-probability-philosopher Nassim Taleb.


pages: 543 words: 147,357

Them And Us: Politics, Greed And Inequality - Why We Need A Fair Society by Will Hutton

Abraham Maslow, Alan Greenspan, Andrei Shleifer, asset-backed security, bank run, banking crisis, Bear Stearns, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Blythe Masters, Boris Johnson, bread and circuses, Bretton Woods, business cycle, capital controls, carbon footprint, Carmen Reinhart, Cass Sunstein, centre right, choice architecture, cloud computing, collective bargaining, conceptual framework, Corn Laws, Cornelius Vanderbilt, corporate governance, creative destruction, credit crunch, Credit Default Swap, debt deflation, decarbonisation, Deng Xiaoping, discovery of DNA, discovery of the americas, discrete time, disinformation, diversification, double helix, Edward Glaeser, financial deregulation, financial engineering, financial innovation, financial intermediation, first-past-the-post, floating exchange rates, Francis Fukuyama: the end of history, Frank Levy and Richard Murnane: The New Division of Labor, full employment, general purpose technology, George Akerlof, Gini coefficient, Glass-Steagall Act, global supply chain, Growth in a Time of Debt, Hyman Minsky, I think there is a world market for maybe five computers, income inequality, inflation targeting, interest rate swap, invisible hand, Isaac Newton, James Dyson, James Watt: steam engine, Japanese asset price bubble, joint-stock company, Joseph Schumpeter, Kenneth Rogoff, knowledge economy, knowledge worker, labour market flexibility, language acquisition, Large Hadron Collider, liberal capitalism, light touch regulation, Long Term Capital Management, long term incentive plan, Louis Pasteur, low cost airline, low interest rates, low-wage service sector, mandelbrot fractal, margin call, market fundamentalism, Martin Wolf, mass immigration, means of production, meritocracy, Mikhail Gorbachev, millennium bug, Money creation, money market fund, moral hazard, moral panic, mortgage debt, Myron Scholes, Neil Kinnock, new economy, Northern Rock, offshore financial centre, open economy, plutocrats, power law, price discrimination, private sector deleveraging, proprietary trading, purchasing power parity, quantitative easing, race to the bottom, railway mania, random walk, rent-seeking, reserve currency, Richard Thaler, Right to Buy, rising living standards, Robert Shiller, Ronald Reagan, Rory Sutherland, Satyajit Das, Savings and loan crisis, shareholder value, short selling, Silicon Valley, Skype, South Sea Bubble, Steve Jobs, systems thinking, tail risk, The Market for Lemons, the market place, The Myth of the Rational Market, the payments system, the scientific method, The Wealth of Nations by Adam Smith, three-masted sailing ship, too big to fail, unpaid internship, value at risk, Vilfredo Pareto, Washington Consensus, wealth creators, work culture , working poor, world market for maybe five computers, zero-sum game, éminence grise

This led to the development of mathematically computed value at risk (VaR), which was based on the same assumptions about random walks, efficient markets and bell curves that had been used when pricing derivatives. The VaR figure is the maximum amount a financial institution might lose on any given day with a probability of 95 per cent or higher. Dick Fuld, the CEO of Lehman Brothers, could comfort himself throughout 2007 and even the first half of 2008 that his bank was exposed to less than $100 million of VaR on any given day (between 95 and 99 per cent confidence level). Merrill Lynch had less than $50 million VaR throughout 2006 (95 per cent confidence level), which climbed above $75 million only in the third quarter of 2007.33 In fact VaR was so wide of the mark that Merrill Lynch was to report a $2 billion loss for that quarter and an $8.7 billion loss for the next quarter before being taken over by the Bank of America nine months later.

Now, with the benefit of hindsight, it seems obvious that larger banks that are deemed too big to fail should be obliged to carry more capital to underwrite their business. In 2004 the view of both regulators and the bankers themselves was that the large banks would have more diversified risks, and so needed less capital. The claim was also made that they utilised sophisticated risk-management techniques, notably value at risk (VaR), which allegedly allowed them to assess risk more accurately than smaller banks and thus had a more carefully calibrated view of the amount of capital they needed. The argument went that such banks should be permitted to assess the riskiness of their own loans and then negotiate their capital needs with the regulator.

., 156–7 Oxbridge/top university entry, 293–4, 306 Oxford University, 261 Page, Scott, 204 Paine, Tom, 347 Pareto, Vilfredo, 201–2 Paribas, 152, 187 Parkinson, Lance-Bombardier Ben, 13 participation, political, 35, 86, 96, 99 Paulson, Henry, 177 Paulson, John, 103, 167–8 pay of executives and bankers, 3–4, 5, 6–7, 22, 66–7, 138, 387; bonuses, 6, 25–6, 41, 174–5, 176, 179, 208, 242, 249, 388; high levels/rises of, 6–7, 13, 25, 82–3, 94, 172–6, 216, 296, 387, 393; Peter Mandelson on, 24; post-crash/bail-outs, 176, 216; in private equity houses, 248; remuneration committees, 6, 82, 83, 176; shared capitalism and, 66, 93; spurious justifications for, 42, 78, 82–3, 94, 176, 216 pension, state, 81, 372, 373 pension funds, 240, 242 Pettis, Michael, 379–80 pharmaceutical industry, 219, 255, 263, 265, 267–8 Phelps, Edmund, 275 philanthropy and charitable giving, 13, 25, 280 Philippines, 168 Philippon, Thomas, 172–3 Philips Electronics, Royal, 256 Pimco, 177 piracy, 101–2 Plato, 39, 44 Player, Gary, 76 pluralist state/society, x, 35, 99, 113, 233, 331, 350, 394 Poland, 67, 254 political parties, 13–14, 340, 341, 345, 390; see also under entries for individual parties political system, British: see also democracy; centralised constitution, 14–15, 35, 217, 334; coalitions as a good thing, 345–6; decline of class-based politics, 341; devolving of power to Cardiff and Edinburgh, 15, 334; expenses scandal, 3, 14, 217, 313, 341; history of (to late nineteenth-century), 124–30; lack of departmental coordination, 335, 336, 337; long-term policy making and, 217; monarchy and, 15, 312, 336; politicians’ lack of experience outside politics, 338; required reforms of, 344–8; select committee system, 339–40; settlement (of 1689), 125; sovereignty and, 223, 346, 347, 378; urgent need for reform, 35, 36–7, 218, 344; voter-politician disengagement, 217–18, 310, 311, 313–14, 340 Pommerehne, Werner, 60 population levels, world, 36 Portsmouth Football Club, 352 Portugal, 108, 109, 121, 377 poverty, 278–9; child development and, 288–90; circumstantial causes of, 26, 283–4; Conservative Party and, 279; ‘deserving’/’undeserving’ poor, 276, 277–8, 280, 284, 297, 301; Enlightenment views on, 53, 55–6; need for asset ownership, 301–3, 304; political left and, 78–83; the poor viewed as a race apart, 285–7; as relative not absolute, 55, 84; Adam Smith on, 55, 84; structure of market economy and, 78–9, 83; view that the poor deserve to be poor, 25, 52–3, 80, 83, 281, 285–8, 297, 301, 387; worldwide, 383, 384 Power2010 website, 340–1 PR companies and media, 322, 323 Press Complaints Commission (PCC), 325, 327, 331–2, 348 preventative medicine, 371 Price, Lance, 328, 340 Price, Mark, 93 Prince, Chuck, 184 printing press, 109, 110–11 prisoners, early release of, 11 private-equity firms, 6, 28–9, 158, 172, 177, 179, 205, 244–9, 374 Procter & Gamble, 167, 255 productive entrepreneurship, 6, 22–3, 28, 29–30, 33, 61–2, 63, 78, 84, 136, 298; in British history (to 1850), 28, 124, 126–7, 129; due desert/fairness and, 102–3, 105–6, 112, 223, 272, 393; general-purpose technologies (GPTs) and, 107–11, 112, 117, 126–7, 134, 228–9, 256, 261, 384 property market: baby boomer generation and, 372–3; Barker Review, 185; boom in, 5, 143, 161, 183–4, 185–7, 221; bust (1989-91), 161, 163; buy-to-let market, 186; commercial property, 7, 356, 359, 363; demutualisation of building societies, 156, 186; deregulation (1971) and, 161; Japanese crunch (1989-92) and, 361–2; need for tax on profits from home ownership, 308–9, 373–4; property as national obsession, 187; residential mortgages, 7, 183–4, 186, 356, 359, 363; securitised loans based mortgages, 171, 186, 188; shadow banking system and, 171, 172; ‘subprime’ mortgages, 64, 152, 161, 186, 203 proportionality, 4, 24, 26, 35, 38, 39–40, 44–6, 51, 84, 218; see also desert, due, concept of; contributory/discretionary benefits and, 63; diplomacy/ international relations and, 385–6; job seeker’s allowance as transgression of, 81; left wing politics and, 80; luck and, 73–7, 273; policy responses to crash and, 215–16; poverty relief systems and, 80–1; profit and, 40, 388; types of entrepreneurship and, 61–2, 63 protectionism, 36, 358, 376–7, 378, 379, 382, 386 Prussia, 128 Public Accounts Committee, 340 Purnell, James, 338 quantitative easing, 176 Quayle, Dan, 177 race, disadvantage and, 290 railways, 9, 28, 105, 109–10, 126 Rand, Ayn, 145, 234 Rawls, John, 57, 58, 63, 73, 78 Reagan, Ronald, 135, 163 recession, xi, 3, 8, 9, 138, 153, 210, 223, 335; of 1979-81 period, 161; efficacy of fiscal policy, 367–8; VAT decrease (2009) and, 366–7 reciprocity, 43, 45, 82, 86, 90, 143, 271, 304, 382; see also desert, due, concept of; proportionality Reckitt Benckiser, 82–3 Regional Development Agencies, 21 regulation: see also Bank of England; Financial Services Authority (FSA); Bank of International Settlements (BIS), 169, 182; Basel system, 158, 160, 163, 169, 170–1, 196, 385; big as beautiful in global banking, 201–2; Big Bang (1986), 90, 162; by-passing of, 137, 187; capital requirements/ratios, 162–3, 170–1, 208; dismantling of post-war system, 149, 158, 159–63; economists’ doubts over deregulation, 163; example of China, 160; failure to prevent crash, 154, 197, 198–9; Glass-Steagall abolition (1999), 170, 202–3; light-touch, 5, 32, 138, 151, 162, 198–9; New Deal rules (1930s), 159, 162; in pharmaceutical industry, 267–8; as pro-business tool, 268–70; proposed Financial Policy Committee, 208; required reforms of, 267, 269–70, 376, 377, 384, 392; reserve requirements scrapped (1979), 208; task of banking authorities, 157; Top Runner programme in Japan, 269 Reinhart, Carmen, 214, 356 Repo 105 technique, 181 Reshef, Ariell, 172–3 Reuters, 322, 331 riches and wealth, 11–13, 272–3, 283–4, 387–8; see also pay of executives and bankers; the rich as deserving of their wealth, 25–6, 52, 278, 296–7 Rickards, James, 194 risk, 149, 158, 165, 298–302, 352–3; credit default swaps and, 151, 152, 166–8, 170, 171, 175, 176, 191, 203, 207; derivatives and see derivatives; distinction between uncertainty and, 189–90, 191, 192–3, 196–7; employment insurance concept, 298–9, 301, 374; management, 165, 170, 171, 189, 191–2, 193–4, 195–6, 202, 203, 210, 354; securitisation and, 32, 147, 165, 169, 171, 186, 188, 196; structured investment vehicles and, 151, 165, 169, 171, 188; value at risk (VaR), 171, 192, 195, 196 Risley, Todd, 289 Ritchie, Andrew, 103 Ritter, Scott, 329 Robinson, Sir Gerry, 295 Rogoff, Ken, 214, 356 rogue states, 36 Rolling Stones, 247 Rolls-Royce, 219, 231 Rome, classical, 45, 74, 108, 116 Roosevelt, Franklin D., 133, 300 Rothermere, Viscount, 327 Rousseau, Jean-Jacques, 56, 58, 112 Rousseau, Peter, 256 Rowling, J.K., 64, 65 Rowthorn, Robert, 292, 363 Royal Bank of Scotland (RBS), 25, 150, 152, 157, 173, 181, 199, 251, 259; collapse of, 7, 137, 150, 158, 175–6, 202, 203, 204; Sir Fred Goodwin and, 7, 150, 176, 340 Rubin, Robert, 174, 177, 183 rule of law, x, 4, 220, 235 Russell, Bertrand, 189 Russia, 127, 134–5, 169, 201, 354–5, 385; fall of communism, 135, 140; oligarchs, 30, 65, 135 Rwandan genocide, 71 Ryanair, 233 sailing ships, three-masted, 108 Sandbrook, Dominic, 22 Sands, Peter (CEO of Standard Chartered Bank), 26 Sarkozy, Nicolas, 51, 377 Sassoon, Sir James, 178 Scholes, Myron, 169, 191, 193 Schumpeter, Joseph, 62, 67, 111 science and technology: capitalist dynamism and, 27–8, 31, 112–13; digitalisation, 34, 231, 320, 349, 350; the Enlightenment and, 31, 108–9, 112–13, 116–17, 121, 126–7; general-purpose technologies (GPTs), 107–11, 112, 117, 126–7, 134, 228–9, 256, 261, 384; increased pace of advance, 228–9, 253, 297; nanotechnology, 232; New Labour improvements, 21; new opportunities and, 33–4, 228–9, 231–3; new technologies, 232, 233, 240; universities and, 261–5 Scotland, devolving of power to, 15, 334 Scott, James, 114–15 Scott Bader, 93 Scott Trust, 327 Second World War, 134, 313 Securities and Exchanges Commission, 151, 167–8 securitisation, 32, 147, 165, 169, 171, 186, 187, 196 self-determination, 85–6 self-employment, 86 self-interest, 59, 60, 78 Sen, Amartya, 51, 232, 275 service sector, 8, 291, 341, 355 shadow banking system, 148, 153, 157–8, 170, 171, 172, 187 Shakespeare, William, 39, 274, 351 shareholders, 156, 197, 216–17, 240–4, 250 Sher, George, 46, 50, 51 Sherman Act (USA, 1890), 133 Sherraden, Michael, 301 Shiller, Robert, 43, 298, 299 Shimer, Robert, 299 Shleifer, Andrei, 62, 63, 92 short selling, 103 Sicilian mafia, 101, 105 Simon, Herbert, 222 Simpson, George, 142–3 single mothers, 17, 53, 287 sixth form education, 306 Sky (broadcasting company), 30, 318, 330, 389 Skype, 253 Slim, Carlos, 30 Sloan School of Management, 195 Slumdog Millionaire, 283 Smith, Adam, 55, 84, 104, 112, 121, 122, 126, 145–6 Smith, John, 148 Snoddy, Ray, 322 Snow, John, 177 social capital, 88–9, 92 social class, 78, 130, 230, 304, 343, 388; childcare and, 278, 288–90; continued importance of, 271, 283–96; decline of class-based politics, 341; education and, 13, 17, 223, 264–5, 272–3, 274, 276, 292–5, 304, 308; historical development of, 56–8, 109, 115–16, 122, 123–5, 127–8, 199; New Labour and, 271, 277–9; working-class opinion, 16, 143 social investment, 10, 19, 20–1, 279, 280–1 social polarisation, 9–16, 34–5, 223, 271–4, 282–5, 286–97, 342; Conservative reforms (1979-97) and, 275–6; New Labour and, 277–9; private education and, 13, 223, 264–5, 272–3, 276, 283–4, 293–5, 304; required reforms for reduction of, 297–309 social security benefits, 277, 278, 299–301, 328; contributory, 63, 81, 283; flexicurity social system, 299–301, 304, 374; to immigrants, 81–2, 282, 283, 284; job seeker’s allowance, 81, 281, 298, 301; New Labour and ‘undeserving’ claimants, 143, 277–8; non-contributory, 63, 79, 81, 82; targeting of/two-tier system, 277, 281 socialism, 22, 32, 38, 75, 138, 144, 145, 394 Soham murder case, 10, 339 Solomon Brothers, 173 Sony, 254–5 Soros, George, 166 Sorrell, Martin, 349 Soskice, David, 342–3 South Korea, 168, 358–9 South Sea Bubble, 125–6 Spain, 123–4, 207, 358–9, 371, 377 Spamann, Holger, 198 special purpose vehicles, 181 Spitzer, Matthew, 60 sport, cheating in, 23 stakeholder capitalism, x, 148–9 Standard Oil, 130–1, 132 state, British: anti-statism, 20, 22, 233–4, 235, 311; big finance’s penetration of, 176, 178–80; ‘choice architecture’ and, 238, 252; desired level of involvement, 234–5; domination of by media, 14, 16, 221, 338, 339, 343; facilitation of fairness, ix–x, 391–2, 394–5; investment in knowledge, 28, 31, 40, 220, 235, 261, 265; need for government as employer of last resort, 300; need for hybrid financial system, 244, 249–52; need for intervention in markets, 219–22, 229–30, 235–9, 252, 392; need for reshaping of, 34; pluralism, x, 35, 99, 113, 233, 331, 350, 394; public ownership, 32, 240; target-setting in, 91–2; threats to civil liberty and, 340 steam engine, 110, 126 Steinmueller, W.


The Concepts and Practice of Mathematical Finance by Mark S. Joshi

Black-Scholes formula, Brownian motion, correlation coefficient, Credit Default Swap, currency risk, delta neutral, discrete time, Emanuel Derman, financial engineering, fixed income, implied volatility, incomplete markets, interest rate derivative, interest rate swap, London Interbank Offered Rate, martingale, millennium bug, power law, quantitative trading / quantitative finance, risk free rate, short selling, stochastic process, stochastic volatility, the market place, time value of money, transaction costs, value at risk, volatility smile, yield curve, zero-coupon bond

Theta The derivative of the price of an instrument with respect to time. Trigger option An option that requires the holder to buy or sell an asset at a fixed price according to the level of some reference rate. Value at risk (or VAR) The amount that a portfolio can lose over some period of time with a given probability. For example, the amount the bank can lose in one day with 5% probability. VAR Short for value at risk. Variance Variance is defined as Var(X) _ ]E((X - ]E(X))2). Vanna The derivative of the Vega with respect to the underlying. Vega The derivative of the price of an instrument with respect to volatility. Yield The effective interest rate receivable by purchasing a bond.

Index N, 65 N(0, 1), 57 N(µ, a2), 98 or-field, 458 accreting notional, 429 admissible exercise strategy, see exercise strategy, admissible almost, 257 almost surely, 99 American, 10 American option, see option, American amortising notional, 429 annualized rates, 302 annuity, 308 .anti-thetic sampling, 192 arbitrage, 19-20, 27-29,429 and bounding option prices, 29-39 arbitrage-free price, 45, 46 arbitrageur, 12-13, 18 Arrow-Debreu security, 152 at-the-forward, 31 at-the-money, 30, 66 auto cap, 429 bank, 12 barrier option, see option, barrier basis point, 429 basket option, 261 Bermudan option, see option, Bermudan Bermudan swaption, see swaption, Bermudan BGM, 429 implementation of, 450-453 BGM model, 322-355 automatic calibration to co-terminal swaptions, 342 long steps, 337 running a simulation, 337-342 BGM/J, 429 BGM/J model, see BGM model bid-offer spread, 21 Black formula, 173, 310-311 approximate linearity, 356 approximation for swaption pricing under BGM model, 341 Black-Scholes formula, see option, call, Black-Scholes formula for Black-Scholes density, 188 Black-Scholes equation, 69, 160, 161 for options on dividend-paying assets, 123 higher-dimensional, 271 informal derivation of, 114-116 rigorous derivation, 116-119 solution of, 119-121 with time-dependent parameters, 164 Black-Scholes formula, 65 Black-Scholes model, 74, 113, 430 Black-Scholes price, 19 Black-Scholes model, 76 bond, 4 6, 430 callable, 301 convertible, 7, 430 corporate, 7 government, 1 premium, 2 riskless, 5, 7 zero-coupon, 5, 24-26, 28, 302, 433 Brownian bridge, 230 Brownian motion, 97-100, 101, 107, 142, 260, 430 correlated, 263 higher-dimensional, 261-263 Buffett, Warren, 2 bushy tree, see tree, non-recombining calibration to vanilla options using jump-diffusion, 377 call, 301 call option, see option, call callable bond, see bond, callable cap, 309, 430 caplet, 309-311, 430 strike of, 309 caption, 326, 430 cash bond, 26, 430 Central Limit theorem, 56, 60 Central Limit Theorem, 64 Central Limit theorem, 278,463 central method, 238 533 Index 534 CEV, see constant elasticity of variance chain rule, 106 for stochastic calculus, 109 characteristic function, 408 Cholesky decomposition, 227 cliquet, 425, 430 call, 425 optional, 426 put, 425 CMS, see swap, constant maturity co-initial, 317, 340 co-terminal, 317, 340 commodities, 123 complete market, 152, 430 compound optionality, 426 conditional probability, 460 consol, 430 constant elasticity of variance, 113 constant elasticity of variance process, 355 constant maturity swap, see swap, constant maturity contingent claim, 152, 430 continuously compounding rate, 25, 26 control variate and pricing of Bermudan swaptions, 351 on a tree, 288 convenience yield, 123 convexity, 35-37, 81 as a function of spot price in a log-type model, 383 correlation, 466 between forward rates, 321, 335 correlation matrix, 268, 466 cost of carry, 123 coupon, 4, 301, 430 covariance, 466 covariance matrix, 466 and implementing BGM, 343 crash, 10, 86 credit default swap, 23 credit rating, 316, 430 cumulative distribution function, 461 cumulative normal function, 65, 435, 437 default, 1 deflated, 168 Delta, 76, 80,430 and static replication, 246, 248 Black-Scholes formula for call option, 80 integral expression for, 189 Delta hedging, see hedging, Delta dependent, 461 derivative, 10, 430 credit, 11 weather, 11 Derman-Kani implied tree, 381 deterministic future smile, 244, 426 digital, 430 digital option, see option, digital dimensionality, 224, 438 dimensionality reduction, 229 discount curve, 431 discretely compounding money market account, 324 displaced diffusion model, 355 distribution log-normal, see log-normal distribution diversifiable risk, 431 diversification, 8 dividend, 7, 431 scrip, 25 dividend rate, 25 dividends and the Black-Scholes equation, 121-123 drift, 60, 111 of a forward rate under BGM, 330 real-world, 64 Dupire model, 381 dynamic replication, see replication, dynamic early exercise, 68 equivalent martingale measure for a tree with jumps, 363 equivalent probability measures, see probability measure, equivalence European, 10 European contingent claim, 116 exercise, 10 exercise boundary, 289 exercise region, 289 exercise strategy admissible, 286 expectation, 431, 462 conditional, 155 fat tails, 85, 431, 464 Feynman-Kac theorem, 161 fickle, 377 filtration, 143, 154, 162 first variation, see variation, first fixed leg, 306 fixed rate, 431 floating, 300 floating leg, 306 floating rate, 431 floating smile, see smile, floating floor, 309,431 floorlet, 309, 431 floortion, 326, 431 forward contract, 9, 22, 181, 431 and risk-neutrality, 137 value of, 26 forward price, 26, 31 forward rates, 303-305 forward-rate agreement, 23, 304, 431 Fourier transform, 395, 408 FRA, see forward-rate agreement free boundary value problem, 290 Gamma, 77, 80, 431 and static replication, 246, 248 Black-Scholes formula for a call option, 80 non-negativity of, 384 Index Gamma distribution, 402 Gamma function, 402 incomplete, 405 Gaussian distribution, 57, 103 Gaussian random variable synthesis of, 191 gearing, 300 geometric Brownian motion, 111, 114 gilt, 314 Girsanov transformation, 214 Girsanov's theorem, 158, 166, 210-213, 368, 390, 431 higher-dimensional, 267-271 Greeks, 77-83, 431 and static replication, 246, 248 computation of on a tree, 186 of multi-look options, 236-238 heat equation, 119, 120-121 Heath, Jarrow & Morton, 322 hedger, 12-13, 18 hedging, 4, 8, 11, 67-68, 431, 441 and martingale pricing, 162-164 Delta, 18-19, 68, 73, 76, 115, 118, 162 exotic option under jump-diffusion, 535 Ito's Lemma, 106-110 application of, 111-114 multi-dimensional, 264 joint density function, 464 joint law of minimum and terminal value of a Brownian motion with drift, 213 without drift, 208 jump-diffusion model, 87, 364-381 and deterministic future smiles, 244 and replication of American options, 293 price of vanilla options as a function of jump intensity, 374 pricing by risk-neutral evaluation, 364-367 jump-diffusion process, 361 jumps, 86-88 jumps on a tree, 362 Kappa, 79 knock in, 202 knock out, 202 knock-in option, see option, barrier knock-out option, see option, barrier kurtosis, 85, 432, 464 375 Gamma, 77 in a one-step tree, 44-45 in a three-state model, 49 in a two-step model, 51 of exotic options, 424 vanilla options in a jump-diffusion world, 372 Vega, 79 hedging strategy, 17-18, 44, 76 stop-loss, 143 hedging, discrete, 76 HJM model, 322 homogeneity, 274, 281, 383 implied volatility, see volatility, implied importance sampling, 193 in-the-money, 30 incomplete, 431 incomplete market, 50, 361, 367-375, 389, 390 incomplete model, 89 incremental path generation, see path generation, incremental independent, 461 information, 2, 4, 113, 140-145, 162, 401 conditioning on, 145 insider trading, 3 insurance, 12 inverse cumulative normal function, 192, 435, 436 inverse floater, 359 Ito, 97 Ito calculus higher-dimensional, 261, 263-266 Ito process, 106, 154 law of large numbers, 69, 191, 462 law of the minimum of a Brownian motion drift, 215, 216 law of the unconscious statistician, 463 Leibniz rule, 110 leveraging, 300 LIBID, 432 LIBOR, 302, 315, 432 LIBOR market model, 322 LIBOR-in-arrears, 312-313 LIBOR-in-arrears caplet pricing by BGM, 326 LIBOR-in-arrears FRA pricing by BGM, 326 likelihood ratio, 195, 237 liquidity, 21 Lloyds, 6 log-normal distribution, 61 log-normal model, 58 approximation by a tree, see tree, approximating a log-normal model for stock price movements, 112 log-type model, 382-385 long, 21, 432 low-discrepancy numbers, 193 the pricing of exotic options, 445-447 lucky paths, 369 marginal distribution, 465 Margrabe option, see option, Margrabe market efficiency, 2-4 weak, 3, 4, 99 market maker, 74 market model, 432 market price of risk, 89, 112 Index 536 Markov property, 3, 98, 99 strong, 210 martingale, 129, 145, 432 and no arbitrage, 146 continuous, 154-160 discrete, 146 higher-dimensional, 267 martingale measure, 148 choice of, 376 uniqueness, 150 martingale pricing and time-dependent parameters, 164-165 based on the forward, 172-175 continuous, 157-160 discrete, 145-154 equivalence to PDE method, 161-162 with dividend-paying assets, 171 martingale representation theorem, 162 maturity, 5 maximal foresight, 296 mean-reverting process, 390 measure change, 368 model risk, 244 moment, 432 moment matching, 193 and pricing of Asian options, 231-233 money-market account, 26, 114, 430 moneyness, 385 monotonicity theorem, 27 Monte Carlo simulation, 69, 462 and price of exotic options using a jump-diffusion model, 379 and pricing of European options, 191 computation of Greeks, 194-195 variance reduction, 192 Moro, 435 mortgage, 301 multi-look option, see option, multi-look Name, 6 natural payoff, 330 NFLWVR, 132, 135 no free lunch principle, 19 no free lunch with vanishing risk, see NFLWVR no-arbitrage, 45 non-recombining tree, see tree, non-recombining normal distribution, see Gaussian distribution, 461 notional, 304 numeral e, 168, 174, 310, 312, 314, 324 change of, 167 numerical integration and pricing of European options, 187-190 option, 9-12 American, 68, 144, 284, 429 boundary conditions for PDE, 290 lower bounds by Monte Carlo, 293-295 PDE pricing, 289-291 pricing on a tree, 287-289 replication of, 291-293 seller's price, 297 theoretical price of, 287 upper bounds by Monte Carlo, 295-297 American digital, 219 American put, 219 Asian, 222, 429 pricing by PDE or tree, 233-234 static replication of, 249-251 barrier, 69, 429 definition, 202-204 price of down-and-out call, 217, 218 basket, 261, 429 Bermudan, 284,429 binary, 429 call, 10, 181, 430 American, 32 Black-Scholes formula for, 65, 160 down-and-in, 202 down-and-out, 202 formula for price in jump-diffusion model, 366, 367 pay-off, 29 perpetual American, 299 pricing under Black-Scholes, 114 chooser, 294 continuous barrier expectation pricing of, 207-208, 216-219 PDE pricing of, 205-207 static replication of, 244-247, 252-256 static replication of down-and-out put, 244-246 continuous double barrier static replication, 246-247 digital, 83, 257 call, 83 put, 83 digital call, 181 Black-Scholes formula for price of, 183 digital put, 181 Black-Scholes formula for price of, 183 discrete barrier, 222 static replication of, 247-249 double digital, 130 European, 431 exotic, 10, 87 Monte Carlo, 444445 pricing under jump-diffusion, 379-381 knock-in, 431 knock-out, 69, 432 Margrabe, 260, 273-275 model-independent bounds on price, 29-39 multi-look, 223 Parisian, 432 path-dependent, 223 and risk-neutral pricing, 223-225 static replication of, 249-251 power call, 182 put, 10, 181,432 Black-Scholes formula for, 65 pay-off, 30 Index quanto, 260, 275-280 static replication of up-and-in put with barrier at strike, 251-252 trigger, 433 vanilla, 10 with multiple exercise dates, 284 out-of-the-money, 30 path dependence weak, 225 path generation, 226-230 incremental, 228 using spectral theory, 228 path-dependent exotic option, see option, path-dependent pathwise method, 195, 236 PDE methods and the pricing of European options, 195-196 Poisson process, 364 positive semi-definite, 467 positivity, 7, 28 predictable, 162 predictor-corrector, 340 present valuing, 302 previsible, 370 pricing arbitrage-free, 22 principal, 5, 301 probability risk-neutral, see risk-neutral probability probability density function, 461 probability measure, 458 equivalence, 147 product rule for Ito processes, 110 pseudo-square root, 468 put option, see option, put put-call parity, 30, 65, 67 put-call symmetry, 252-256 quadratic variation, 100, see variation, quadratic quanto call, 277 quanto drift, 276 quanto forward, 277 quanto option, see option, quanto quasi Monte Carlo, 194 Radon-Nikodym, 214 Radon-Nikodym derivative, 213 random time, 88, 143 random variable, 459 real-world drift, see drift, real-world recombining trees implementing, 443 reflection principle, 208-210 replication, 23, 116 and dividends, 122 and the pricing of European options, 196-198 classification of methods, 257 dynamic, 198, 257 in a one-step tree, 48-49 537 in a three-state model, 50 semi-static and jump-diffusion models, 381 static, 198 feeble, 257 mezzo, 257 strong, 243, 257 weak, 243, 257 repo, 315 restricted stochastic-volatility model, see Dupire model reverse option, 320 reversing pair, 319 Rho, 79 rho, 432 risk, 1-2, 8, 9 diversifiable, 8-9 purity of, 9 risk neutral, 19 risk premium, 46, 60, 64, 111, 119,432 risk-neutral distribution, 64 risk-neutral density as second derivative of call price, 137 in Black-Scholes world, 139 risk-neutral expectation, 64 risk-neutral measure, 148, 432 completeness, 166 existence of, 129 uniqueness, 166 risk-neutral pricing, 64 65, 140 higher-dimensional, 267-271 risk-neutral probability, 47, 52, 54, 59, 128 risk-neutral valuation, 59 in a one-step tree, 45-48 in a three-state model, 50 in jump models, 86 two-step model, 52 riskless, 1 riskless asset, 28 Rogers method for upper bounds by Monte Carlo, 295, 350 sample space, 458 self-financing portfolio, 28, 116-117, 128, 163, 369 dynamic, 28 share, 6-7, 432 share split, 57 short, 432 short rate, 25, 433 short selling, 21 simplex method, 295 skew, 433, 464 smile, 74-77 displaced-diffusion, 356,420 equity, 421 floating, 88, 385, 407, 413-414 foreign exchange, 413 FX, 424 interest-rate, 355-357, 424 jump-diffusion, 378, 415 sticky, 88, 413-414 sticky-delta, 413 538 smile (cont.) stochastic volatility, 398, 416 time dependence, 414-415 Variance Gamma, 406,417 smile dynamics Deiman-Kani, 420 displaced-diffusion, 420 Dupire model, 420 equity, 421 FX, 424 interest-rate, 424 jump-diffusion, 415 market, 413-415 model, 415-421 stochastic volatility, 416 Variance Gamma, 417 smoothing operator, 120 spectral theory, 228 speculator, 12, 18 split share, see share split spot price, 31 square root of a matrix, 467 standard error, 191 standard deviation, 463 static replication, see replication, static stepping methods for Monte Carlo, 439 stochastic, 433 stochastic calculus, 97 stochastic differential equation, 105 for square of Brownian motion, 107 stochastic process, 102-106, 141 stochastic volatility, 88, 389 and risk-neutral pricing, 390-393 implied, 400 pricing by Monte Carlo, 391-394 pricing by PDE and transform methods, 395-398 stochastic volatility smiles, see smile, stochastic volatility stock, 6-7, 433 stop loss hedging strategy, 18 stopping time, 143, 286, 346 straddle, 182, 257 Index lower bound via local optimization, 347 lower bounds by BGM, 345-349 pricing by BGM, 325 upper bounds by BGM, 349-352 cash-settled, 327 European, 310 price of, 313-314 payer's, 309,432 pricing by BGM, 323 receiver's, 309, 432 swaptions rapid approximation to price in a BGM model, 340 Taylor's theorem, 67, 80, 108 term structure of implied volatilities, 334 terminal decorrelation, 339, 352 Theta, 79, 433 and static replication, 246, 248 time homogeneity, 33, 333 time value of money, 24-26 time-dependent volatility and pricing of multi-look options, 235 Tower Law, 155 trading volatility, see volatility, trading of trading volume, 401 transaction costs, 21, 76, 90, 91 trapezium method, 188 tree with multiple time steps, 50-55 and pricing of European options, 183-186 and time-dependent volatility, 184 approximating a log-normal model, 60-68 approximating a normal model, 55-58 higher-dimensional, 277-280 non-recombining, 184 one-step, 44-50 risk-neutral behaviour, 61 trinomial, 184 with interest rates, 58-59 trigger FRA, 318 trigger swap, 325 pricing by BGM, 325 trinomial tree, see tree, trinomial strike, 10, 433 strong static replication, see replication, static, strong sub-replication, 369-375 super-replication, 369-375 swap, 300, 305-309, 433 constant maturity, 328 payer's, 306, 432 pricing by BGM, 323 receiver's, 306, 432 value of, 308 swap rate, 433 swap-rate market model, 340 swaption, 301, 309, 433 Bermudan, 301, 310, 342 and factor reduction, 352-355 lower bound via global optimization, 347 underlying, 10 uniform distribution, 461 valuation risk-neutral, see risk-neutral valuation value at risk, 433 Vanna, 433 VAR, 433 variance, 433, 463 Variance Gamma mean rate, 402 variance rate, 403 Variance Gamma density, 408 Variance Gamma model, 88, 404-407 and deterministic future smiles, 244 Variance Gamma process, 401-403 Index variation, 157, 367 first, 99, 367, 409 quadratic, 368 second, see variation, quadratic Vega, 79, 82, 433 integral expression for, 189 Vega hedging, see hedging, Vega volatility, 60, 65, 66, 73-74, 111 Black-Scholes formula as linear function of, 66 forward, 426 implied, 73, 197 instantaneous curve, 320, 333 539 root-mean-square, 320 time-dependence and tree-pricing, 294 trading of, 73 volatility surface, 363 weak static replication, see replication, static, weak Wiener measure, 141, 142 yield, 5, 24, 433 annualized, 25 yield curve, 319, 433 zero-coupon bond, see bond, zero-coupon

C.2 Expectations and moments 463 As well as needing the notion of the average of a random variable, we also need to understand how likely it is to stray from the average. This notion is captured by the variance, defined to be equal to Var(X) = E((X - E(X))2). (C.10) As the expectation of a positive quantity, the variance is always positive. Its square root is called the standard deviation. We have trivially that Var(X) = E(X2) - E(X)2, (C.11) and Var(aX) = a2Var(X). (C.12) We also have that if X and Y are independent then Var(X + Y) = Var(X) + Var(Y). (C.13) When evaluating expectations of functions of X we have the handy result, sometimes called the law of the unconscious statistician, that IE(g(X)) = J fx(s)g(s)ds (C.14) for any function g.


pages: 272 words: 19,172

Hedge Fund Market Wizards by Jack D. Schwager

asset-backed security, backtesting, banking crisis, barriers to entry, Bear Stearns, beat the dealer, Bernie Madoff, Black-Scholes formula, book value, British Empire, business cycle, buy and hold, buy the rumour, sell the news, Claude Shannon: information theory, clean tech, cloud computing, collateralized debt obligation, commodity trading advisor, computerized trading, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, diversification, diversified portfolio, do what you love, Edward Thorp, family office, financial independence, fixed income, Flash crash, global macro, hindsight bias, implied volatility, index fund, intangible asset, James Dyson, Jones Act, legacy carrier, Long Term Capital Management, managed futures, margin call, market bubble, market fundamentalism, Market Wizards by Jack D. Schwager, merger arbitrage, Michael Milken, money market fund, oil shock, pattern recognition, pets.com, Ponzi scheme, private sector deleveraging, proprietary trading, quantitative easing, quantitative trading / quantitative finance, Reminiscences of a Stock Operator, Right to Buy, risk free rate, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Rubik’s Cube, Savings and loan crisis, Sharpe ratio, short selling, statistical arbitrage, Steve Jobs, systematic trading, technology bubble, transaction costs, value at risk, yield curve

In terms of volatility-adjusted leverage, their risk exposure had actually gone up. I notice that you use VAR as a risk measurement. Aren’t you concerned that it can sometimes be very misleading regarding portfolio risk? Value at Risk (VAR) can be defined as the loss threshold that will not be exceeded within a specified time interval at some high confidence level (typically, 95 percent or 99 percent). The VAR can be stated in either dollar or percentage terms. For example, a 3.2 percent daily VAR at the 99 percent confidence level would imply that the daily loss is expected to exceed 3.2 percent on only 1 out of 100 days. To convert a VAR from daily to monthly, we multiply it by the square root of 22 (the approximate number of trading days in a month).

See also Kelly criterion Trading around a position Trading book rules Trading pits, changes since electronic trading Trading rules vs. guidelines Trading style development Trend following Trend-neutral model Trend vs. countertrend methodologies Trinity Industries 200-day moving average Tyco Value and Special Situation Investing course Value at Risk (VAR) Value investing Value Investors Club Value-weighted indexes Vidich, Joe Volatility Volatility assumption Volatility vs. risk Warburg Securities Warrants Weighted indexes Wells Fargo Williams, Greyson Wolfe, Tom Woodriff, Jaffray on data mining on fund capacity statistical prediction research Woodriff Trading Worst of option XLP index You Can Be a Stock Market Genius (Greenblatt)

The VAR provides a worst-case loss estimate assuming future volatility and correlation levels look like the past. The main reason the VAR gets a bad name is because people don’t understand it. VAR does exactly what it says on the tin. Which is? It tells you how volatile your current portfolio was in the past. That is all. VAR is entirely backward looking. You have to recognize that the future will be different. If I think the world in the future will be highly volatile, then I will run a current VAR that is relatively low because I think the future will be more volatile than the past. VAR gets a bad name because people manage risk by it, and the shortcoming is that volatilities and correlations can change very radically on an existing portfolio vis-à-vis what they were in the past.


pages: 461 words: 128,421

The Myth of the Rational Market: A History of Risk, Reward, and Delusion on Wall Street by Justin Fox

"Friedman doctrine" OR "shareholder theory", Abraham Wald, activist fund / activist shareholder / activist investor, Alan Greenspan, Albert Einstein, Andrei Shleifer, AOL-Time Warner, asset allocation, asset-backed security, bank run, beat the dealer, behavioural economics, Benoit Mandelbrot, Big Tech, Black Monday: stock market crash in 1987, Black-Scholes formula, book value, Bretton Woods, Brownian motion, business cycle, buy and hold, capital asset pricing model, card file, Carl Icahn, Cass Sunstein, collateralized debt obligation, compensation consultant, complexity theory, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, democratizing finance, Dennis Tito, discovery of the americas, diversification, diversified portfolio, Dr. Strangelove, Edward Glaeser, Edward Thorp, endowment effect, equity risk premium, Eugene Fama: efficient market hypothesis, experimental economics, financial innovation, Financial Instability Hypothesis, fixed income, floating exchange rates, George Akerlof, Glass-Steagall Act, Henri Poincaré, Hyman Minsky, implied volatility, impulse control, index arbitrage, index card, index fund, information asymmetry, invisible hand, Isaac Newton, John Bogle, John Meriwether, John Nash: game theory, John von Neumann, joint-stock company, Joseph Schumpeter, junk bonds, Kenneth Arrow, libertarian paternalism, linear programming, Long Term Capital Management, Louis Bachelier, low interest rates, mandelbrot fractal, market bubble, market design, Michael Milken, Myron Scholes, New Journalism, Nikolai Kondratiev, Paul Lévy, Paul Samuelson, pension reform, performance metric, Ponzi scheme, power law, prediction markets, proprietary trading, prudent man rule, pushing on a string, quantitative trading / quantitative finance, Ralph Nader, RAND corporation, random walk, Richard Thaler, risk/return, road to serfdom, Robert Bork, Robert Shiller, rolodex, Ronald Reagan, seminal paper, shareholder value, Sharpe ratio, short selling, side project, Silicon Valley, Skinner box, Social Responsibility of Business Is to Increase Its Profits, South Sea Bubble, statistical model, stocks for the long run, tech worker, The Chicago School, The Myth of the Rational Market, The Predators' Ball, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, Thorstein Veblen, Tobin tax, transaction costs, tulip mania, Two Sigma, Tyler Cowen, value at risk, Vanguard fund, Vilfredo Pareto, volatility smile, Yogi Berra

In the early 1990s, as banks and their customers struggled to get a handle on the risks posed by their derivatives deals, most turned to an approach called “value at risk,” or VaR. This name was new—coined at J. P. Morgan in the 1980s—but it described what Harry Markowitz had dubbed “semi-variance” in 1959. It was the downside risk, a quantitative measure of how much a portfolio could drop on a bad day. It was possible to estimate a value at risk that took into account some of the wondrous and fat-tailed behavior of actual financial markets, but that required guesswork and judgment. To persuade a wary CEO to green-light a derivatives deal or convince a bank regulator that capital reserves were enough to cover potential losses, one needed a standardized VaR model like the RiskMetrics version peddled by J.

Taleb’s harder-to-argue-away concern was that widespread use of VaR made markets riskier. A drop in the price of a security raised the value at risk of a portfolio containing that security. If a bank or hedge fund was trying to keep the VaR below a certain level, it might then have to sell off other securities to push the VaR back down. That put downward pressure on the prices of those other securities, which in turn threatened to start the cycle over again. Just as selling by portfolio insurers trying to protect their clients from price declines had driven down prices yet further in 1987, VaR had the potential to exacerbate a downturn.

Department of Agriculture, 195 U.S. Department of Labor, 138 U.S. House of Representatives, 40 U.S. Naval Research Laboratory, 67 U.S. Senate, 40, 123–24 U.S. Steel, 15, 277 U.S. Supreme Court, 276 “The Use of Knowledge in Society” (Hayek), 91–92 utility theory, 10, 30, 51–52, 75 value at risk (VaR), 238–39 value investing, 116–18, 206, 215–16, 226, 255, 260 Vanguard, 129, 131 variance, 134–35, 138–39 Veblen, Thorstein, 30–31, 33–34, 76, 157 Viniar, David, 316 Vishny, Robert, 252–55, 300 volatility, 138–39, 144–45, 197, 233–34 Von Neumann-Morgenstern expected utility, 51–52, 54, 75, 80, 176–77, 193 wage controls, 136–37 Waldmann, Robert, 251 Waldrop, Mitchell, 302, 304–5 The Wall Street Journal, 15, 17–18, 26, 112, 163, 219, 224, 231–32, 235, 262–63 Wall $treet Week, 163 Wallis, W.


Money and Government: The Past and Future of Economics by Robert Skidelsky

"Friedman doctrine" OR "shareholder theory", Alan Greenspan, anti-globalists, Asian financial crisis, asset-backed security, bank run, banking crisis, banks create money, barriers to entry, Basel III, basic income, Bear Stearns, behavioural economics, Ben Bernanke: helicopter money, Big bang: deregulation of the City of London, book value, Bretton Woods, British Empire, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, constrained optimization, Corn Laws, correlation does not imply causation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, David Graeber, David Ricardo: comparative advantage, debt deflation, Deng Xiaoping, Donald Trump, Eugene Fama: efficient market hypothesis, eurozone crisis, fake news, financial deregulation, financial engineering, financial innovation, Financial Instability Hypothesis, forward guidance, Fractional reserve banking, full employment, Gini coefficient, Glass-Steagall Act, Goodhart's law, Growth in a Time of Debt, guns versus butter model, Hyman Minsky, income inequality, incomplete markets, inflation targeting, invisible hand, Isaac Newton, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kenneth Rogoff, Kondratiev cycle, labour market flexibility, labour mobility, land bank, law of one price, liberal capitalism, light touch regulation, liquidationism / Banker’s doctrine / the Treasury view, liquidity trap, long and variable lags, low interest rates, market clearing, market friction, Martin Wolf, means of production, Meghnad Desai, Mexican peso crisis / tequila crisis, mobile money, Modern Monetary Theory, Money creation, Mont Pelerin Society, moral hazard, mortgage debt, new economy, Nick Leeson, North Sea oil, Northern Rock, nudge theory, offshore financial centre, oil shock, open economy, paradox of thrift, Pareto efficiency, Paul Samuelson, Phillips curve, placebo effect, post-war consensus, price stability, profit maximization, proprietary trading, public intellectual, quantitative easing, random walk, regulatory arbitrage, rent-seeking, reserve currency, Richard Thaler, rising living standards, risk/return, road to serfdom, Robert Shiller, Ronald Reagan, savings glut, secular stagnation, shareholder value, short selling, Simon Kuznets, structural adjustment programs, technological determinism, The Chicago School, The Great Moderation, the payments system, The Wealth of Nations by Adam Smith, Thomas Malthus, Thorstein Veblen, tontine, too big to fail, trade liberalization, value at risk, Washington Consensus, yield curve, zero-sum game

In this way, mark-to-market accounting increases volatility by artificially enlarging and contracting balance sheets. But its doompotential was blithely ignored. Value at risk modelling was used by banks to assess the amount of risk they faced on their portfolios. A VaR measure takes a given portfolio, a time horizon and a probability level p, and spits out a threshold value of loss for that portfolio, representing a ‘realistic’ worst-case scenario. For example, if your portfolio has a one-day 1 per cent VaR of $1 million, this means that 99 per cent of the time your portfolio will not fall in value by more than $1 million over a one-day period. VaR measures were popular, as they condensed lots of risk modelling into a single, easily comprehensible figure.

Hyman Minsky, an economist whose work was completely ignored until after the crash, argued that financial stability leads inevitably to financial fragility, as optimism turns to ‘speculative euphoria’ and markets become ‘dominated by speculation about sentiments and movements in the market rather than about fundamental asset values’.18 But these arguments had no place in the neo-classical hegemony and so, despite its glaring theoretical gaps, the EMH became the intellectual underpinning of financial market deregulation. 313 M ac roe c onom ic s i n t h e C r a s h a n d A f t e r , 2 0 0 7 – ‘Mark-to-market (M2M) and value at risk (VaR) frameworks offer accurate measures of value and thus are appropriate ways of managing risk’ Mark-to-market accounting aims to estimate the ‘fair value’ of an asset by reference to its current market price, rather than what it cost the investor to buy. If an investor owns ten shares of a stock bought for $4 a share and that stock now trades at $6, its mark-to-market value is 50 per cent more than its book value.

., 179 Erie Canal, 90 Eshag, Eprime, 71 European Central Bank, 139, 188, 198, 217, 242–3, 253, 254, 361 institutional constraints on, 50, 234, 242, 249, 274–5 misreading of Eurozone crisis, 275 quantitative easing (QE) by, 273–4 on ‘stress testing’, 364 taxing of ‘excess’ reserves, 266 use of LTROs, 257 European Commission, 139, 3612, 365 European Exchange Rate Mechanism, 188 European Investment Bank, 354 European Union (EU, formerly EEC), 153, 318, 379, 383 Financial Stability Board (FSB), 363 ‘Four Freedoms’, 375 lack of state, 376 Single Resolution Board, 365 Eurozone current account imbalances, 333, 334, 335, 336–7, 341–2 Juncker investment programme, 274 proposed European Monetary Fund, 376, 382 structural flaw in, 341, 375–7 two original sins of, 274, 376–5 Eurozone debt crisis (2010–12), 50, 223, 377, 382 and double-dip recession, 241, 242–3, 274 ECB’s misreading of, 275 and financial crowding-out theory, 234 and Greece, 32, 224, 224–5, 226, 233, 235, 242–3, 243, 365 and ‘troika’, 32, 139, 243 469 i n de x exchange-rate policy, 127–8, 139 and Congdon’s ‘real balance effect’, 285 and domestic interest rates, 251 fixed rates under Bretton Woods, 16, 159, 161, 162, 168 floating rates from 1970s, 16–17, 184 and Friedman, 182 IMF ‘scarce currency’ clause, 380–81 Nixon’s dollar devaluation (1971), 153, 154, 165 and quantitative easing, 267, 267 sterling crisis (1951), 145 sterling devaluation (November 1967), 152 sterling-dollar peg (from 1949), 148, 150, 152 sterling/franc/deutschmark devaluations (1949), 152 ‘Triffin paradox’, 161, 165 ‘expansionary fiscal consolidation’, 192, 225, 231 Fabian socialism, 96 Fama, Eugene, 208, 311–12, 313 Fanny Mae, 217, 256, 309, 320 fascism, 13, 98, 131, 175 Federal Reserve, US and 2008 crash, 50, 217, 254, 256 AIG bail-out (2008), 325 Federal Open Market Committee (FOMC), 185–6 and Great Depression, 104–6 inflation targeting, 188 and monetarism, 185–6, 188 monetary policy in 1950s, 146 ‘Operation Twist’, 268 quantitative easing (QE) by, 256–7, 273–4 ‘Reserve Position Doctrine’ (1920s), 103–4 and under-consumption theory, 298 Ferguson, Niall, 73, 79, 80, 91 financial collapse (2007–8) acute phase, 218–20, 223 ‘Austrian’ explanation, 104, 303 banks as proximate cause, 343, 361, 365 Bear Stearns rescue, 217 British analogies with Greece, 235 British debate after, 225–8 causes of, 3–4, 343–4, 365, 366, 368 central bank responses, 3, 217, 219, 234–5, 253–4, 254, 256–8, 359 comparative recovery patterns, 241–4, 242, 273, 273–4 compared to 1929 crash, 218 Conservative narrative, 226–8, 229–31, 233, 234–5, 237–9 and crisis of conservative economics, 17 and embedded leverage, 318, 322, 325 five distinct stages of crisis, 216–19 ‘global imbalances’ explanation, 11, 331, 333, 336–43, 337 government responses, 3, 217–18, 219–20, 221–36, 237–47 Hayekian view of cause, 303 hysteresis after, 239–41, 240, 241, 370 inequality as deeper cause of, 299–306, 368 Lehman Brothers bankruptcy, 3, 50, 217, 365 leverage (debt to equity) ratios on eve of, 317–18 liquidity-solvency confusion, 317 outbreaks of populism following, 13, 371–3, 376, 383 post-crash deficit, 226–33, 229, 237–8 private debt as proximate cause, 3–4 470 i n de x stagnation of real earnings as deep cause, 4, 303, 367 standard account of origins of, 3–4 as test of two theories, 2–3, 76 theoretical and policy responses, 10, 129, 219–20, 223–36, 237–47 see also austerity policy and under-consumption theory, 303–6 US sub-prime mortgage market, 3, 216, 304–5, 309, 323, 328, 341 see also Great Recession (2008–9) Financial Services Authority, U K, 321–2, 330 financial system and causes of 2008 collapse, 3, 4–5, 253, 307–9, 361 and crisis of conservative economics, 17 deregulation, 307–9, 310–16, 318–22, 328, 332–3, 384 East Asian financial crisis (1997–8), 202, 339, 371, 382 ‘Efficient Market Hypothesis’ (EMH), 311–13, 321–2, 328, 388 ‘financialization’ of the economy, 5, 305, 307–9, 366–7 fraud and criminality, 3, 4, 5, 7, 328, 350, 365–6, 367 and free-market orthodoxy, 5, 308–16 loosening of moral restraints, 319 mark-to-market (M2M) framework, 314 offshore euro-dollar market, 308, 332 privatised gain and socialised loss, 319–20 released from national regulation (1980s/90s), 131, 318–22 structural power of finance, 6–7, 14, 309 systemic under-estimation of risk, 314–16, 316*, 320–22, 323, 329–30 Thatcher’s Big Bang (1980s), 319 tradable public debt instruments, 43, 80–81 Turner’s ‘financial intensity’ concept, 366 unrealism of assumptions, 310–16 value at risk (VaR) framework, 314–15, 315, 330 ‘Washington consensus’ deregulation, 198, 200 see also banks FinTech, 356 First World War, 86, 95, 106–7, 374, 375 ‘fiscal consolidation’, 10–11, 129, 225 Darling’s plan (2009), 225–6 ‘expansionary’, 192, 225, 231 and Osborne, 227–8, 229–30, 231, 233, 237–9, 243–4, 244, 245 fiscal policy and 2008 collapse, 10, 217–18, 219–20, 223–36, 265–6, 273–4, 286 ‘Barber boom’, 167, 168 during Blair-Brown years, 221–4, 223, 225–6, 227 British experience (1692–2012), 77 Congdon’s total rejection of, 280, 285–6 ‘crowding out’ argument, 83–4, 109–11, 226, 233–5 current and capital spending, 107–8, 114, 142, 155–6, 193, 221–3, 237–8, 355–7 directing flow of new spending, 286–7 fiscal multiplier, 110–11, 125–6, 133–6, 138, 230–31, 233, 235, 244–5 471 i n de x fiscal policy – (cont.) in inter-war Britain, 106–17 and Keynesian economics, 2–3, 109, 111, 114–17, 125–7, 129–31, 133–4, 137–8, 173, 278 Keynesian full employment phase (1945–60), 141–8 Krugman’s ‘confidence fairy’, 117 Lawson counterrevolution, 185, 192–3, 222, 358 legacy of monetarism, 190–93 May Committee (1931), 112 national income accounts, 138 New Classical view of, 200 in new macroeconomic constitution, 351–2, 355–7, 360–61 nineteenth-century theory of, 9, 29 post-Keynesian disablement of, 193, 221, 258, 304, 328 pre-crash orthodoxy, 221–2, 223–4, 230–31 Public Sector Borrowing Requirement (PSBR), 155–6 see also balanced budget theory; public investment; taxation Fisher, Irving, 9, 52, 61, 99, 280 ‘compensated dollar’ scheme, 66 equation of exchange, 62–4, 71–2, 258, 278–9, 283, 284, 287 QTM formulation, 62–7, 71–2 and quantitative easing, 258, 278–9 Santa Claus money, 62–4, 258, 278–9 Fitch (CR A), 329 France assignats in 1790s, 64–5 and gold standard, 50, 102, 104, 127 ‘indicative planning’ system, 150 ‘physiocrats’in, 81 protectionism in late nineteenthcentury, 59 state holding companies, 356 statism in, 140, 144 university campus revolts (1968), 164 Freddie Mac, 217, 256, 309, 320 free trade, xviii, 9, 58–9, 76, 79, 81–2 abandoned in Britain (1932), 113 general presumption in favour of, 377 and Hume’s ‘price-specie-flow’ mechanism, 37–8, 53, 54, 104, 332 and Irish potato famine, 15 List’s ‘infant industry’ argument, 88–9, 90, 378–7 and nationalist–globalist split, 371–3 and post-war liberalization, 16, 374 and presumption of peace, 379 repeal of Corn Laws (1846), 15, 85 Ricardo’s doctrine of comparative advantage, 88, 378, 379, 379 US conversion to (1940s), 90 Freiburg School, 140 Friedman, Milton adaptive expectations theory, 180–81, 183, 194, 206–11 and Cartesian distinction, 22 as Fisher’s heir, 278 The Great Contraction (with Schwartz; 1865), 105 idea of ‘helicopter money’, 63 and monetary base, 185, 280 and Mont Pelerin Society, 176–7 and ‘natural’ rate of unemployment, 163, 177, 181, 195, 206, 208 onslaught on Keynesianism, 170, 174, 177–83, 261 ‘permanent income hypothesis’ (1957), 178, 183 and Phillips Curve, 38, 180–81, 194, 206–8, 207 472 i n de x policy implications of work of, 182–3 political motives of, 177, 183–4 and quantity theory, 61, 70, 177–9, 182, 183, 194 ‘stable demand function for money’, 179 view of Great Depression, 104–6, 179, 183, 256, 276, 278 weaknesses in arguments of, 183 Frydman, Roman, 389 Fullarton, John, 49 Funding for Lending programme, 265–6 G20 Financial Stability Board, 363 summits (2009/10), 219–20, 223, 225 G7 finance ministers meeting (February 2010), 224–5 Galbraith, James, 303, 361 game theorists, 389 Gasperin, Simone, 357* Geddes, Sir Eric, 108 German Historical School, 88–9 Germany and 2008 crash, 217, 218, 243 current account surplus, 333, 334, 341, 342, 380, 381 employer–union bargains, 147, 167 and Eurozone crisis, 341, 365, 376, 377 and Great Depression, 97, 111, 129–30 growth Keynesianism (1960–70), 153–4 high growth rates in 1950/60s, 149, 156 Hitler’s reduction in unemployment, 111, 112, 129–30 hyperinflation of early 1920s, 275 as Keynesian in 1960s, 140 nineteenth-century expansion and unification, 89, 91 ‘ordo-liberalism’ in, 140 post-war modernization/catch-up, 156–7 protectionism in late nineteenthcentury, 59 return to gold standard (1924), 102 ‘Rhenish capitalism’ model, 154 Giffen, Robert, 51 Giles, Chris, 219, 302 Gini coefficient, 299, 300 Gladstone, William, 42–3, 86 Glass–Steagall Act (1933), 319, 361, 362 global imbalances basic theory of, 335–6 and capital account liberalization, 318–19 capital flight, 59, 334, 337, 341, 343 Eurozone see Eurozone: current account imbalances as explanation for 2007–8 crash, 11, 331, 333, 336–43, 337 and financial deregulation, 318–19, 332–3 and First World War, 95 increases in pre-crash years, 333, 333–4, 334, 335 problematic nature of, 333–4 reserve accumulation, 336, 337–41 ‘saving glut’ vs ‘money’ glut, 338–41, 342 structural causes still in place, 344 US dollar as main reserve currency, 338 global warming, 383 globalization, 17, 300, 334–5 absence of the state, 350, 373, 375–6 anti-globalist movements, 371–2, 373 first age of, 51, 55, 57, 59, 374, 375 473 i n de x globalization – (cont.) future of, 382–4 Geneva and Seattle protests (1998/99), 371 and inflation rate, 252–3 and lower wages in developed world, 252–3, 300, 379 nationalist-globalist split, 371–3 ‘neo-liberal’ agenda of IMF, 139, 181, 318–19 popular protest against, 351, 371–2 resurgence of after Cold War, 374 Rodrik’s ‘impossible trinity’, 375 gold, 23, 24, 25, 28, 35, 37 new gold production, 51, 52, 55, 62 gold standard, xviii, 1, 9, 27, 29, 338 and Britain, 9, 42, 43, 44, 45–50, 53, 57–9, 80, 101, 102, 113 collapse of US exchange standard (1971), 160, 165 commitment to convertibility, 55–6 and Cunliffe model, 54–5, 102 depressions in later nineteenthcentury, 51–2 dysfunctional after First World War, 95, 97 final suspension in Britain (1931), 113, 125 Fisher’s ‘compensated dollar’ scheme, 66 Hume’s ‘price-specie-flow’ mechanism, 37–8, 53, 54, 104, 285, 332 and international bond markets, 92 as international by 1880s, 50–52 Keynes on, 58, 101, 127 Kindleberger thesis, 58–9 move to ‘managed’ system, 71, 99–100 replaces silver standard (1690s), 42, 43 restored (1821), 48 return to in 1920s, 102, 104, 107 suspension during Napoleonic wars, 43, 45–7 suspension of convertibility (1919), 101–2 triumph of by mid-nineteenthcentury, 44, 50 working and design of, 52–9 as working in tandem with empire, 57, 58 Goldberg, Michael D., 389 Goldman Sachs, 315 Goodhart, Charles, 168, 187 Graeber, David, 28 Great Depression (1929–32), 9, 13, 96, 97–8, 110–13, 127 compared to 2008 crash, 218 Friedman-Schwartz view, 104–6, 179, 183, 256, 276, 278 impact on US policy-makers in 2008 period, 256, 275, 278 left-wing explanations of, 298 rise in inequality in lead-up to, 289 and second wave of collectivism, 15–16 Great Moderation (early 1990s–2007), 4, 53, 202, 278 economic problems during, 348 financial deregulation during, 318–22, 328 financial innovation during, 322–8 and independent central banks, 215 inflation during, 106, 215, 216, 252–3, 253, 348, 359, 360 international financial network, 309, 318–28 output growth during, 215, 253, 348 Great Recession (2008–9), xviii Congdon’s view of, 281–2, 287 co-ordinated global response, 219–20, 383 decline in productivity after, 305–6 474 i n de x initial signs of recovery (2009), 218–19, 225, 226 monetary interpretation of, 105, 106 ‘premature withdrawal’ of fiscal stimulus, 219–20, 223–36, 245, 352 reform agenda after, 361–8 rise in inequality in lead-up to, 289–90, 299–300 see also financial collapse (2007–8) Greece and Eurozone debt crisis, 32, 224, 224–5, 226, 233, 235, 242–3, 243, 337, 341, 365 in gold standard era, 59 Greenspan, Alan, 188, 313 Hamilton, Alexander, 88, 90, 92 Hammond, Philip, 236, 352 Hannover Re scandal, 329 Harrison, George, 105 Harrod, Roy, 123 Harvey, John, 333, 387 Hawtrey, Ralph, 109–10, 280 Hayek, Friedrich, 33, 46, 177, 195, 350, 367 founds Mont Pelerin Society, 176 ‘over-consumption’ theory, 296 The Road to Serfdom (1944), 16, 175–6 on Wall Street Crash, 104 Heath, Edward, 167–8 Heckscher, Eli, 37 Help to Buy programme, 265, 266 Henderson, Hubert, 109 Henderson, W.


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Safe Haven: Investing for Financial Storms by Mark Spitznagel

Albert Einstein, Antoine Gombaud: Chevalier de Méré, asset allocation, behavioural economics, bitcoin, Black Swan, blockchain, book value, Brownian motion, Buckminster Fuller, cognitive dissonance, commodity trading advisor, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, delayed gratification, diversification, diversified portfolio, Edward Thorp, fiat currency, financial engineering, Fractional reserve banking, global macro, Henri Poincaré, hindsight bias, Long Term Capital Management, Mark Spitznagel, Paul Samuelson, phenotype, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, random walk, rent-seeking, Richard Feynman, risk free rate, risk-adjusted returns, Schrödinger's Cat, Sharpe ratio, spice trade, Steve Jobs, tail risk, the scientific method, transaction costs, value at risk, yield curve, zero-sum game

., 87–95 and median CAGR of SPX portfolio, 132–135 in no‐crash bootstrap, 145–148 portfolio effect when mitigating systematic risk, 128 as prototype, 105–109, 120 reshuffling returns in, 142, 143 risk‐mitigation scoreboard for, 131 standardizing degree of risk in, 134 Strategic safe havens, 110, 147 Strong emergence, 126 Success in investing, 18 Sun Tzu, 12 Susceptibility of payoffs, 169 Syllogisms, 18–22 Synergy, 158 Systematic risk, 116, 117, 120 T Tactical safe havens, 110, 147 Tail hedging, 177–178 Taleb, Nassim Nicholas, 5, 25, 80–81, 165 Talus bones, 22 Target panic, 13 Taxonomy of safe havens: and differences among safe havens, 103–104 and diversification, 115–117 essentialist, 99–103 imposters in, 111–117 and irony of risk mitigation, 109–111 phenotypes, 105–109 and real‐world frequency distributions, 117–122 Tell, William, 86, 93, 196 Tenbagger insurance safe haven, 109, 120 “That Which Is Seen and That Which Is Not Seen” (Bastiat), 153–154 Theory of relativity, for investing, 127, 151, Se also Holism Thinking: about risk mitigation, 194 specialized vs. localized, 157–160 Thorp, Ed, 80 “Those Wonderful Tenbaggers” (Lynch), 109 Thus Spake Zarathustra (Nietzsche), 58 Time: and ergodic processes, 75 evolution of capital base through, 77 investing as process through, 14–15 in multiverse concept, 64 with Nietzsche's demon, 72 and Petersburg paradox, 56 warping of payoff profiles over, 168 Time average, 75, 80–81 Titanic, 109 Transparency: with bootstrapping, 128 in safe haven investing, 25 Trend‐following CTA strategies, 108, 175–178, 182–184 Trim tabs, 158 Tsatsouline, Pavel, 106–107 Tversky, Amos, 53 Type I errors, 169–170 Type II errors, 170 U The Unbearable Lightness of Being (Kundera), 200 Uncertainty: decision‐making under, 53 modeling behavior under, 36 Universa Investments, 3–8, 183–184, 196 Unsafe havens, 112, 114 Uranus, 167 US Treasuries, 170–175, 182–184 Utility, 35–37 Utility function of wealth, 39 V Value: book, 104 expected, see Expected value fair, 34–35, 42–43 of gold, 179–180 intrinsic, 104, 180 known by net portfolio effect, 151 Value at risk (VaR), 83 “Value” effect, 150 Value investing, 104 VaR (value at risk), 83 Vertical profit and loss, 48 Volatility, 17, 115, 116 Volatility tax, 75 Von Clausewitz, Carl, 12 Von Neumann, John, 37 Vulcan, 167, 175, 187 W Wagers. See also specific wagers expected value of, 32–33, 39–42 fair value of, 34–35 rebalancing size of, 126, 150.

This 5th percentile outcome is known in the investment industry as value at risk, or the 5% VaR. In our case, where the sample space of outcomes is well defined, this 5% VaR is an excellent way to define the degree of risk in the game. After all, we defined risk as exposure to bad contingencies, or these worst potential paths. So think of these worst paths as Graham's margin of safety; the higher that 5th percentile outcome, the greater the margin of safety, and the safer the wager. In the real world, of course, these bad outcomes are not so well‐defined. There is a spuriousness to the measurement of a portfolio's VaR, and an extremely naïve lack of robustness to its estimation, to the point that its use can do far more harm than good when actually relied upon.

There is a spuriousness to the measurement of a portfolio's VaR, and an extremely naïve lack of robustness to its estimation, to the point that its use can do far more harm than good when actually relied upon. This will be a very important point for us in later chapters. But for our dice games, the 5% VaR is an ideal proxy for risk. As we can see, beyond the Kelly optimal bet size, it gets far riskier, and too much of a good thing is not such a good thing. You can overplay your hand, and thus creep too close to the edge of Bernoulli Falls. Yet, curiously, the tools of modern finance would insist that leverage is basically always good when the expected arithmetic returns are positive like this.


pages: 430 words: 140,405

A Colossal Failure of Common Sense: The Inside Story of the Collapse of Lehman Brothers by Lawrence G. Mcdonald, Patrick Robinson

"World Economic Forum" Davos, Alan Greenspan, AOL-Time Warner, asset-backed security, bank run, Bear Stearns, Black Monday: stock market crash in 1987, book value, business cycle, Carl Icahn, collateralized debt obligation, corporate raider, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, diversification, fixed income, Glass-Steagall Act, high net worth, hiring and firing, if you build it, they will come, it's over 9,000, junk bonds, London Interbank Offered Rate, Long Term Capital Management, margin call, money market fund, moral hazard, mortgage debt, naked short selling, negative equity, new economy, Ronald Reagan, Savings and loan crisis, short selling, sovereign wealth fund, value at risk

Risk managers in Lehman Brothers were guided, advised, regulated, trapped, imprisoned, and threatened on pain of torture and death by a tyrant who stood in their back office with a bullwhip and branding irons. His name was VaR. His strength was beyond that of a normal man; he could terrify legions and lay down the law in a manner that made empires shudder. VaR had a brain the size of a caraway seed and the imagination of a parsnip. The acronym that provides his name comes from value at risk, a technique used to estimate the probability of portfolio losses based on the statistical analysis of historical price trends and volatilities. It measures the worst expected loss under normal market conditions over a specific time interval at a given confidence level.

This meant that if a security did not have a history of volatility, it would irrevocably be marked as riskless despite the fact that it currently gazed into the abyss. VaR was a prisoner of its own guidelines. And like all systems that place too much faith in a philosophy, especially one as widely used on a global scale as VaR, it ends up with too much power and influence. It ended up ruling the department it was supposed to assist, because at Lehman no one wanted to be the renegade who stepped over the sacred VaR guidelines. Should there be a disaster, there could be only one scapegoat: the man who kicked over the traces and failed to obey the tried-and-true rules of VaR. Therefore, right or wrong, VaR was obeyed. In our case its flawed reasoning was obvious.

It measures the worst expected loss under normal market conditions over a specific time interval at a given confidence level. Which means it measures both fear and optimism. In this particular instance, VaR knew that the market had no problem with the confidence level of those who bought CDOs. There was as yet no volatility in this market. There had not been any volatility for years. It was a calm, tranquil place, like Nantucket Sound on a still and windless August morning, without even the wash of the passing Martha’s Vineyard ferry. The one great flaw with VaR was its insistence on putting heavy emphasis on recent volatility. This meant that if a security did not have a history of volatility, it would irrevocably be marked as riskless despite the fact that it currently gazed into the abyss.


pages: 364 words: 101,286

The Misbehavior of Markets: A Fractal View of Financial Turbulence by Benoit Mandelbrot, Richard L. Hudson

Alan Greenspan, Albert Einstein, asset allocation, Augustin-Louis Cauchy, behavioural economics, Benoit Mandelbrot, Big bang: deregulation of the City of London, Black Monday: stock market crash in 1987, Black-Scholes formula, British Empire, Brownian motion, business cycle, buy and hold, buy low sell high, capital asset pricing model, carbon-based life, discounted cash flows, diversification, double helix, Edward Lorenz: Chaos theory, electricity market, Elliott wave, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, Fellow of the Royal Society, financial engineering, full employment, Georg Cantor, Henri Poincaré, implied volatility, index fund, informal economy, invisible hand, John Meriwether, John von Neumann, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, market bubble, market microstructure, Myron Scholes, new economy, paper trading, passive investing, Paul Lévy, Paul Samuelson, plutocrats, power law, price mechanism, quantitative trading / quantitative finance, Ralph Nelson Elliott, RAND corporation, random walk, risk free rate, risk tolerance, Robert Shiller, short selling, statistical arbitrage, statistical model, Steve Ballmer, stochastic volatility, transfer pricing, value at risk, Vilfredo Pareto, volatility smile

Time flexibility of market behavior with Titman, Sheridan Tobin, James Topology Trading ranges technical analysis with Trading time Transactions of the American Society of Civil Engineers Tree rings Treynor, Jack Trigonometry Turbulence bursts/pauses of da Vinci on financial heresy of long-term dependence with market behavior with metaphor of Puget Sound currents scaling pattern with wind Ulysses (Joyce) University of British Columbia University of Chicago University of Maryland University of Nottingham University of Paris University of Washington Upensky, J.V. U.S. Agriculture Department U.S. Commodity Futures Trading Commission U.S. Financial Executives Research Foundation U.S. Geological Survey Value at Risk (VAR) Van Ness, John VAR. See Value at Risk Variance Variance gamma process VIX Volatility clustering of Volatility surface Voss, R.F. Wall Street Journal Wallis, James R. Water Resources Research Williams, Albert L. Wind tunnel Wind turbulence World Trade Center attack Zigzag generator fractal geometry with Zipf, George Kingsley formula of power law slope of word frequencies of Zurich Copyright © 2004 by Benoit B.

One of the standard methods relies on—guess what?—Brownian motion. The same false assumptions that underestimate stock-market risk, mis-price options, build bad portfolios, and generally misconstrue the financial world are also built into the standard risk software used by many of the world’s banks. The method is called Value at Risk, or VAR, and it works like this. You start off by deciding how “safe” you need to be. Say you set a 95 percent confidence level. That means you want to structure your bank’s investments so there is, by your models, a 95 percent probability that the losses will stay below the danger point, and only a 5 percent chance they will break through it.

Such reasoning, of course, is little comfort to those wiped out on one of those “improbable,” violent trading days. But the financial industry is supremely pragmatic. While it may genuflect to the old icons, it invests its research dollars in the search for newer, better gods. “Exotic” options, “guaranteed-return” products, “value-at-risk” analysis, and other Wall Street creations have all benefited from this search. Central bankers, too, are pragmatic. After years of accepting the old ways, they have been pushing since 1998 for new, more realistic mathematical models by which a bank should evaluate its risk. These so-called Basle II rules will force many banks to change the way they calculate how much capital they set aside as a cushion against financial catastrophe.


pages: 245 words: 75,397

Fed Up!: Success, Excess and Crisis Through the Eyes of a Hedge Fund Macro Trader by Colin Lancaster

"World Economic Forum" Davos, Adam Neumann (WeWork), Airbnb, Alan Greenspan, always be closing, asset-backed security, beat the dealer, Ben Bernanke: helicopter money, Bernie Sanders, Big Tech, Black Monday: stock market crash in 1987, bond market vigilante , Bonfire of the Vanities, Boris Johnson, Bretton Woods, business cycle, buy the rumour, sell the news, Carmen Reinhart, Chuck Templeton: OpenTable:, collateralized debt obligation, coronavirus, COVID-19, creative destruction, credit crunch, currency manipulation / currency intervention, deal flow, Donald Trump, Edward Thorp, family office, fear index, fiat currency, fixed income, Flash crash, George Floyd, global macro, global pandemic, global supply chain, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, Growth in a Time of Debt, housing crisis, index arbitrage, inverted yield curve, Jeff Bezos, Jim Simons, junk bonds, Kenneth Rogoff, liquidity trap, lockdown, Long Term Capital Management, low interest rates, low skilled workers, margin call, market bubble, Masayoshi Son, Michael Milken, Mikhail Gorbachev, Minsky moment, Modern Monetary Theory, moral hazard, National Debt Clock, Nixon triggered the end of the Bretton Woods system, Northern Rock, oil shock, pets.com, Ponzi scheme, price stability, proprietary trading, quantitative easing, Reminiscences of a Stock Operator, reserve currency, Ronald Reagan, Ronald Reagan: Tear down this wall, Sharpe ratio, short selling, short squeeze, social distancing, SoftBank, statistical arbitrage, stock buybacks, The Great Moderation, TikTok, too big to fail, trickle-down economics, two and twenty, value at risk, Vision Fund, WeWork, yield curve, zero-sum game

The Nasdaq lost 10.5% this week and was nearly 13% below a record high. The VIX hit a high of 49.48 today. We end the month down twenty-nine basis points. We lost less than a percent. We survived. But we have to be ready for Monday. This is going to get worse. Notes 18 “The Promised Land,” Bruce Springsteen. 19 Value at risk (VaR). Chapter 6 Market Crash March 2020 3/3/20—Emergency FOMC meeting; Fed funds cut by 50 bps to 1-1.25%. 3/6/20—Payrolls beat at 273K; unemployment down to 3.5%; average hourly earnings at 3%. 3/6/20—21 cruise ship passengers test positive in California. 3/9/20—10-year yields hit 0.50%. 3/13/20—President Trump declares national emergency; makes $50B in federal funds available. 3/15/20—CDC recommends no gatherings exceeding 50 people; NYC public schools shuttered. 3/15/20—Emergency FOMC meeting; 100 bps cut and targeted at $700B in asset purchases (could be more if needed); reopens the discount window (via 150 bps cut); back to ZIRP and QE.

The markets always spend more time going up than down, so people get used to the comfort, the rhythm, of a bull market. In a bear market, you get much more volatility, lots of head fakes that suck in the weak hands and blow them to bits. You need a lot more patience and discipline. And the Rabbi is right on risk as well. VaR frameworks can’t handle these markets. Markets are not supposed to move like this. VaR models are modulated for normal markets. When your entire risk framework has been turned upside down, it’s hard to assess position size and how to really manage PnL. You need people who have seen this movie before and know the ending. You need extreme mental toughness.

“Okay, we know that VaR19 models can’t handle this, but where will the biggest problems be?” The Rabbi replies, “Most models will fail. Most don’t even include 2008 anymore. I think rates is the biggest problem. They’re not supposed to move like this and anything in RV space has got to be blowing the models up.” “VaR is a moving target right now,” he goes on. “New and higher vols going into the models are pushing bottom-line risk levels higher. And that’s not even taking higher cross-asset correlations into consideration. They’re all going to 1 or −1. Every trader is getting tapped on the shoulder by their risk managers to slash exposures.


pages: 387 words: 119,244

Making It Happen: Fred Goodwin, RBS and the Men Who Blew Up the British Economy by Iain Martin

Alan Greenspan, asset-backed security, bank run, Basel III, Bear Stearns, beat the dealer, Big bang: deregulation of the City of London, Bletchley Park, call centre, central bank independence, computer age, corporate governance, corporate social responsibility, credit crunch, Credit Default Swap, deindustrialization, deskilling, Edward Thorp, Etonian, Eugene Fama: efficient market hypothesis, eurozone crisis, falling living standards, financial deregulation, financial engineering, financial innovation, G4S, Glass-Steagall Act, high net worth, interest rate swap, invisible hand, joint-stock company, Kickstarter, light touch regulation, London Whale, Long Term Capital Management, long term incentive plan, low interest rates, moral hazard, negative equity, Neil Kinnock, Nick Leeson, North Sea oil, Northern Rock, old-boy network, pets.com, proprietary trading, Red Clydeside, shareholder value, The Wealth of Nations by Adam Smith, too big to fail, upwardly mobile, value at risk, warehouse robotics

Increasingly complex risk management systems were evolved in banks to monitor the extent of the exposure and flag warnings, particularly in trading. A lot rested on the adoption of something called VaR, Value at Risk, which teams of risk managers and risk committees inside the banks used to assess, according to various formulae, how much a bank might lose on a particular asset in an emergency.7 It was supposed to be an early warning system. It had been developed by a team at J. P. Morgan in the early 1990s and computer-assisted modelling was integral to the process. VaR was pivotal to the expansion of banks, and what was to follow, because it created a sense of reassurance and confidence.

The bigger question of liquidity – was it certain that a bank with such a large balance sheet could safely lay its hands on enough money – was not regarded by the FSA as a central concern. It was referred to at points in speeches by officials and reports, but never pounced on as a potentially fatal weakness. Anyway, there was no expectation that there would suddenly be a shortage of money. Supposedly the Value at Risk (VaR) system also meant that the banks had clearer sight than ever before of what was at risk as their balance sheets expanded. At Tiner’s management meetings participants remember almost no discussion about the safety of the banks, for good reason: it was hardly mentioned. ‘Prudential’ matters were a low priority barely touched on there or at the board.

., ref 1, ref 2 Agnew, Jonathan, ref 1 AIG, ref 1 Alemany, Ellen, ref 1 Allan, Iain, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7 and CDOs, ref 1, ref 2, ref 3, ref 4 Antonveneta, ref 1 Argus, Don, ref 1 Argyll, 2nd Duke of, ref 1 Armitstead, Louise, ref 1 Arsenal FC, ref 1 Arthur Andersen, ref 1, ref 2 asset-backed securities (ABSs), ref 1, ref 2 AstraZeneca, ref 1, ref 2 Aviva, ref 1 Ayr Bank, ref 1, ref 2 BAA, ref 1 Bailey, Andrew, ref 1, ref 2, ref 3 Bailie Gifford, ref 1 Balfour Beatty, ref 1 Balls, Ed, ref 1, ref 2, ref 3 Bank of America, ref 1 Merrill Lynch sold to, ref 1 Bank Bosses are Criminals, ref 1 Bank of China, ref 1, ref 2 Bank of Credit and Commerce International (BCCI), ref 1, ref 2, ref 3 Bank of England: and banking supervision, see banks: regulation of; Financial Services Authority and County NatWest, ref 1 culpability of, ref 1 Darling reassurance to RBS concerning, ref 1 founding of, ref 1, ref 2 Gieve role in, ref 1 house prices ignored by, ref 1 independence of, ref 1, ref 2, ref 3, ref 4, ref 5 King becomes governor of, ref 1, ref 2 Monetary Policy Committee of, ref 1, ref 2, ref 3 and RBS collapse, ref 1, ref 2 and RBS privatisation, ref 1 and Scottish banks’ own notes, ref 1 and tripartite regulation, ref 1, ref 2, ref 3, ref 4, ref 5; see also Financial Services Authority Bank of Scotland, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6 founding of, ref 1, ref 2 as joint stock-bank, ref 1 modern British banking pioneered by, ref 1 national networks developed by, ref 1 and NatWest, ref 1, ref 2, ref 3, ref 4, ref 5 RBS early rivalry with, ref 1 ‘sues for peace’, ref 1 Whigs distrust, ref 1 see also Halifax; HBOS bankers: accountants versus, ref 1 ‘“canny” Scottish’, ref 1 Labour honours and ennobles, ref 1 large remuneration of, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7, ref 8, ref 9, ref 10, ref 11, ref 12, ref 13 prosecution avoided by, ref 1 banks: auditing of, ref 1; see also banks: regulation/supervision of bailouts of, ref 1, ref 2, ref 3, ref 4, ref 5 passim, ref 1 and Basel regulation, ref 1 and Big Bang, ref 1, ref 2, ref 3, ref 4 Brown wish for competition among, ref 1 Darling promises support for, ref 1 Darling meeting with CEOs of, ref 1 deregulation of, ref 1 foreign investment, presence of, in UK, ref 1 globalised nature of, ref 1 growing profits of, ref 1 innovative activities embraced by, ref 1; see also individual banks and interest rates, ref 1, ref 2, ref 3, ref 4, ref 5 lighter scrutiny of, ref 1; see also Financial Services Authority more credit offered by, ref 1 proposed ring fence for, ref 1, ref 2 regulation/supervision of, ref 1, ref 2, ref 3; see also banks: auditing of; Basel; Financial Services Authority reluctance of, to deal with RBS, ref 1 remodelling of, ref 1 revelations about conduct of, ref 1 ‘too big to fail’, ref 1 tripartite regulation of, ref 1, ref 2, ref 3, ref 4, ref 5; see also Basel; Financial Services Authority UK, balance sheets of, ref 1, ref 2, ref 3, ref 4 UK, clearing, balance sheets of (since 1960), ref 1 UK, growth of, ref 1 UK, steady fall in number of, ref 1 and Value at Risk (VaR), ref 1, ref 2 see also City of London Banque de France, ref 1 Barclays, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7, ref 8, ref 9 and ABN Amro, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7, ref 8 FG hates, ref 1 fines paid by, ref 1 growing profits of, ref 1 Barclays Capital, ref 1, ref 2 Barings, ref 1, ref 2 Basel, ref 1 Bear Stearns, ref 1 Better Regulation Action Plan, ref 1 see also banks: regulation of Better Regulation Task Force, ref 1 Big Bang, ref 1, ref 2, ref 3, ref 4, ref 5 Birmingham and Midshires, ref 1 Black, Joseph, ref 1 Blair, Cherie, ref 1 Blair, Tony, ref 1, ref 2, ref 3 and 1997 election, ref 1 and bank regulation, ref 1 bankers fêted by, ref 1 Brown wants to oust, ref 1 FG Chequers meal with, ref 1 and Gaddafi, ref 1 leadership won by, ref 1 Blank, Victor, ref 1 Bloomberg, ref 1 Blue Arrow affair, ref 1 Blunkett, David, ref 1 BNP Paribas, ref 1, ref 2 boom and bust, ‘end’ of, ref 1, ref 2, ref 3, ref 4, ref 5 Botín, Emilio, ref 1, ref 2, ref 3, ref 4 BP, ref 1 Bradford & Bingley, ref 1, ref 2 Braveheart, ref 1, ref 2, ref 3 Briault, Clive, ref 1, ref 2 Brown, Andrew, ref 1 Brown, Gordon, ref 1, ref 2 passim and 1997 election, ref 1 ‘appalled’ by RBS crisis, ref 1 and bank bailouts, ref 1, ref 2, ref 3 and bank regulation, ref 1, ref 2, ref 3 bankers fêted by, ref 1 becomes Chancellor, ref 1 and BoE independence, ref 1, ref 2, ref 3 boom–bust conference speech of, ref 1 and boom and bust, ‘end’ of, ref 1, ref 2, ref 3, ref 4, ref 5 Chancellorship aspirations of, ref 1 Darling joint press conference with, ref 1 economic growth under, ref 1 father influence on, ref 1 FG compared to, ref 1 and Greenspan, see Greenspan, Alan house prices rise under, ref 1 and interest-rate control, ref 1, ref 2 King relationships with, ref 1 last Mansion House speech of, ref 1 leadership bid lost by, ref 1 and Lloyds–HBOS, ref 1 RBS bailout announced by, ref 1, ref 2 and RBS collapse, ref 1, ref 2 Smith influence on, ref 1 and socialism, ref 1 at university, ref 1 and US politics, ref 1, ref 2 Brown, John, ref 1 Brown, John Ebenezer, ref 1, ref 2 Buccleuch, Duke of, ref 1 Buchan, Colin, ref 1, ref 2, ref 3, ref 4, ref 5 Buffet, Warren, ref 1 Burlington Resources, ref 1 Burns, Robert, ref 1 Burns, Terry, ref 1 Burnside, Howard, ref 1 Burt, Peter, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6 Bush, George H.W., ref 1, ref 2 Bush, George W., ref 1, ref 2, ref 3 Bush, Laura, ref 1 Butler, Lord, ref 1 Cable, Vince, ref 1 Caledonia, naming of, ref 1 Cameron, Donald, ref 1 Cameron, Johnny, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7 passim, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7, ref 8, ref 9 passim, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7 and ABN Amro, ref 1, ref 2, ref 3, ref 4 FG stands by, ref 1 FSA investigates, ref 1, ref 2 at Gogarburn opening, ref 1 and hedging exposure, ref 1 and worsening liquidity situation, ref 1 Camerons of Locheal, ref 1, ref 2, ref 3, ref 4 Campbell, Archibald, see Ilay, Earl of Campbell, John, ref 1, ref 2, ref 3 Canary Wharf, ref 1 Caplan, Rick, ref 1, ref 2, ref 3, ref 4, ref 5 Carpenter, Ben, ref 1, ref 2, ref 3 Charles, Prince of Wales, ref 1, ref 2, ref 3 Charter One, ref 1, ref 2 Chase, ref 1 Chirac, Jacques, ref 1 Chisholm, Andy, ref 1 Churchill, ref 1, ref 2 Churchill, Winston, ref 1 Cicutto, Frank, ref 1, ref 2, ref 3 Citibank, ref 1 Citigroup, ref 1, ref 2, ref 3 Citizens Bank, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7, ref 8, ref 9, ref 10, ref 11, ref 12 and Mellon, ref 1 new CEO for, ref 1 City of Glasgow Bank, ref 1 City of London, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7, ref 8, ref 9, ref 10, ref 11, ref 12, ref 13, ref 14, ref 15 modernisation of, ref 1 see also banks Clarke, Charles, ref 1 Clarke, Ken, ref 1 Clinton, Bill, ref 1, ref 2, ref 3 Clydesdale Bank, ref 1, ref 2, ref 3, ref 4, ref 5 away days of, ref 1 celebrations at, as FG leaves, ref 1 FG becomes CEO of, ref 1 Cochrane, Alan, ref 1 Cole-Hamilton, Richard, ref 1 Coleman, David, ref 1, ref 2 collateralised debt obligations (CDOs), ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7, ref 8, ref 9, ref 10, ref 11 index of, ref 1 varieties of, ref 1 see also sub-prime mortgages Commonwealth Bancorp, ref 1 Community Bancorp, ref 1 Compagnie Bancaire, ref 1 Company of Scotland, ref 1 founding of, ref 1 ‘Competition in UK Banking’, ref 1 Connolly, John, ref 1, ref 2, ref 3, ref 4, ref 5 ConocoPhillips, ref 1 Conservatives, see Tories consumer debt, ref 1 Conti, Tom, ref 1 Cooper, Yvette, ref 1, ref 2 Corbett, R.Y., ref 1 Cornwall, Duchess of, ref 1 Countrywide Financial, ref 1 County NatWest, ref 1, ref 2 Coutts, ref 1, ref 2, ref 3, ref 4, ref 5 Cox, Archie, ref 1 credit crunch, see financial crisis credit default swaps (CDSs), ref 1 Crosby, James, ref 1, ref 2, ref 3, ref 4, ref 5 knighthood lost by, ref 1 Crowe, Brian, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7, ref 8, ref 9, ref 10, ref 11, ref 12 and CDOs, ref 1 and hedging exposure, ref 1 moved to ABN Amro, ref 1 ordination of, ref 1, ref 2 withdrawn from ABN Amro, ref 1 and worsening liquidity situation, ref 1 Crowe, Russell, ref 1 Cruickshank, Don, ref 1 Crutchley, John-Paul, ref 1 Cryan, John, ref 1, ref 2 Cummings, Peter, FSA fines, ref 1 Cummins, John, ref 1 Currie, Jim, ref 1 Daily Telegraph, ref 1, ref 2 Daniels, Eric, ref 1 Darien Scheme, ref 1, ref 2, ref 3, ref 4 Caledonia emerges from, ref 1 Darling, Alistair, ref 1, ref 2 and bank bailouts, ref 1, ref 2, ref 3 banks’ CEOs meet with, ref 1 and Brown–George spat, ref 1 Brown joint press conference with, ref 1 at ECOFIN meeting, ref 1 and FG knighthood, ref 1 and FG pension, ref 1, ref 2, ref 3 FSA and BoE meet with, ref 1 at Gogarburn opening, ref 1 Goodwin meets (2007), ref 1 King follows plan of, ref 1 King relationships with, ref 1 memoirs of, ref 1 MPs briefed on financial crisis by, ref 1 RBS bailout announced by, ref 1, ref 2 and RBS collapse, ref 1 Treasury meeting called by, ref 1 UK banks supported by, ref 1 Darroch, Kim, ref 1 Davidson, Joanna, ref 1, ref 2 Davies, Howard, ref 1, ref 2 Davos, ref 1 de la Renta, Oscar, ref 1 deficit, sharp rise in, ref 1 Deloitte & Touche, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7 Deutsche Bank, ref 1, ref 2 Dewar, Donald, ref 1 Diamond, Bob, ref 1, ref 2, ref 3 forced out of post, ref 1 Dickinson, Alan, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7, ref 8, ref 9, ref 10, ref 11, ref 12, ref 13 Dime Bancorp, ref 1 Direct Line, ref 1, ref 2, ref 3 District Bank, ref 1 Dixon Motors, ref 1 Dixon, Paul, ref 1 Dixon, Simon, ref 1 dotcom bubble, ref 1 Dow Jones, ref 1 Drake-Brockman, Symon, ref 1 Dresdner Kleinwort Wasserstein, ref 1, ref 2 ‘Drivers for Growth’ conference, ref 1 Drummond Bank, ref 1, ref 2, ref 3 Dundas, Lawrence, ref 1 Dundee Banking Company, ref 1 Dutch Central Bank, ref 1 Duthie, Robin, ref 1 East India Company, ref 1 Economic and Financial Affairs Council (ECOFIN), ref 1, ref 2 Economist, ref 1, ref 2 Eden, James, ref 1, ref 2 Elizabeth II, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7 emerging economies, ref 1 Emirates Stadium, ref 1 Enron, ref 1, ref 2 Equitable Life, ref 1 Equivalent Company, ref 1 Ernst & Young, ref 1 euro, see single currency Exchange Rate Mechanism (ERM), ref 1, ref 2 ‘failure of the Royal Bank of Scotland, The’ (FSA), ref 1, ref 2 Fastow, Andy, ref 1 Federal Reserve, ref 1, ref 2, ref 3 Ferguson, Adam, ref 1 Ferguson, Alex, ref 1 Ferguson, William, ref 1 Ferrovial, ref 1 Fidelity, ref 1 Fildes, Christopher, ref 1 Financial Conduct Authority., ref 1 financial crisis: beginning of, ref 1 Darling updates Commons on, ref 1 government spending at start of, ref 1 insurers crack under weight of, ref 1 recessions follow, ref 1 spreads to UK high street, ref 1 studies and reports of, ref 1 Financial Services Authority (FSA), ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7, ref 8, ref 9, ref 10, ref 11, ref 12 ‘Arrow’ reports of, ref 1 and auditors, ref 1 and RBS collapse, ref 1 RBS on watch-list of, ref 1 self-investigation by, ref 1 successors to, ref 1 and tripartite regulation, ref 1, ref 2, ref 3, ref 4, ref 5; see also Bank of England Financial Times, ref 1, ref 2 First Active, ref 1 Fish, Larry, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7, ref 8, ref 9 chairs RBS Americas, ref 1 criticised, ref 1 pension of, ref 1 Fisher, Mark, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7 at Gogarburn opening, ref 1 moved to ABN Amro, ref 1 Fitch Ratings, ref 1, ref 2 Fleming, Ian, ref 1 Fletcher, Andrew, ref 1 Forbes, ref 1 foreign exchange, ref 1, ref 2 Formula 1, ref 1, ref 2 Fortis, ref 1, ref 2, ref 3 Fountain Workshop, ref 1 Franklyn Resources, ref 1 Freshfields, ref 1 Friedrich, Bill, ref 1, ref 2 Fuld, Dick, ref 1 Gaddafi, Muammar, ref 1 Gartmore, ref 1 GE, ref 1 George II, ref 1 George, Eddie, ref 1, ref 2, ref 3, ref 4 Gibson, Mel, ref 1, ref 2 Gieve, John, ref 1 Giles, Chris, ref 1 Gladiator, ref 1 Glass–Steagall Act, ref 1 global financial crisis, see financial crisis Global Transaction Services, ref 1, ref 2 Glyn, Mills & Co., ref 1, ref 2 Goldman Sachs, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6, ref 7, ref 8, ref 9 Goodwin, Andrew (brother), ref 1 Goodwin, Dale (sister), ref 1 Goodwin, Fred: affair of, ref 1, ref 2, ref 3, ref 4 after RBS, ref 1 and away days, ref 1, ref 2 bailout terms heard by, ref 1 Barclays hated by, ref 1 becomes Clydesdale CEO, ref 1 becomes RBS CEO, ref 1 birth of, ref 1 Brown compared to, ref 1 Brown likes, ref 1 Bush dinner guest, ref 1 and car dealership, ref 1 CDO presentation by, ref 1 at CEOs–Darling meeting, ref 1 at CEOs meeting, ref 1 Chequers invitation to, ref 1 ‘classic bully’, ref 1 cleanliness campaigns of, ref 1 at Clydesdale, see Clydesdale Bank colleagues testify to abilities of, ref 1 cult status of, ref 1 at Darling 2008 meeting, ref 1 Darling visited by (2007), ref 1 document criticises management of, ref 1 early life of, ref 1, ref 2 extraordinary general meeting appearance of, ref 1 face-to-face firing disliked by, ref 1, ref 2, ref 3 first job of, ref 1 fixation on detail by, ref 1, ref 2, ref 3, ref 4, ref 5, ref 6 and Forbes, ref 1 ‘Fred the Shred’ nickname of, ref 1, ref 2, ref 3, ref 4 and FSA, ref 1, ref 2 at Gogarburn opening, ref 1 Harvard study on, ref 1, ref 2 ‘has shut out the world’, ref 1 Hester view of, ref 1 ‘I want to be bigger than J.


pages: 807 words: 154,435

Radical Uncertainty: Decision-Making for an Unknowable Future by Mervyn King, John Kay

Airbus A320, Alan Greenspan, Albert Einstein, Albert Michelson, algorithmic trading, anti-fragile, Antoine Gombaud: Chevalier de Méré, Arthur Eddington, autonomous vehicles, availability heuristic, banking crisis, Barry Marshall: ulcers, battle of ideas, Bear Stearns, behavioural economics, Benoit Mandelbrot, bitcoin, Black Swan, Boeing 737 MAX, Bonfire of the Vanities, Brexit referendum, Brownian motion, business cycle, business process, capital asset pricing model, central bank independence, collapse of Lehman Brothers, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, DeepMind, demographic transition, discounted cash flows, disruptive innovation, diversification, diversified portfolio, Donald Trump, Dutch auction, easy for humans, difficult for computers, eat what you kill, Eddington experiment, Edmond Halley, Edward Lloyd's coffeehouse, Edward Thorp, Elon Musk, Ethereum, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, fear of failure, feminist movement, financial deregulation, George Akerlof, germ theory of disease, Goodhart's law, Hans Rosling, Helicobacter pylori, high-speed rail, Ignaz Semmelweis: hand washing, income per capita, incomplete markets, inflation targeting, information asymmetry, invention of the wheel, invisible hand, Jeff Bezos, Jim Simons, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Snow's cholera map, John von Neumann, Kenneth Arrow, Kōnosuke Matsushita, Linda problem, Long Term Capital Management, loss aversion, Louis Pasteur, mandelbrot fractal, market bubble, market fundamentalism, military-industrial complex, Money creation, Moneyball by Michael Lewis explains big data, Monty Hall problem, Nash equilibrium, Nate Silver, new economy, Nick Leeson, Northern Rock, nudge theory, oil shock, PalmPilot, Paul Samuelson, peak oil, Peter Thiel, Philip Mirowski, Phillips curve, Pierre-Simon Laplace, popular electronics, power law, price mechanism, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, railway mania, RAND corporation, reality distortion field, rent-seeking, Richard Feynman, Richard Thaler, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Solow, Ronald Coase, sealed-bid auction, shareholder value, Silicon Valley, Simon Kuznets, Socratic dialogue, South Sea Bubble, spectrum auction, Steve Ballmer, Steve Jobs, Steve Wozniak, Suez crisis 1956, Tacoma Narrows Bridge, Thales and the olive presses, Thales of Miletus, The Chicago School, the map is not the territory, The Market for Lemons, The Nature of the Firm, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Bayes, Thomas Davenport, Thomas Malthus, Toyota Production System, transaction costs, ultimatum game, urban planning, value at risk, world market for maybe five computers, World Values Survey, Yom Kippur War, zero-sum game

Perhaps the personal flying platforms for which we have waited so long will be available to them, or perhaps our descendants will have reverted to the horse and cart after abandoning the use of fossil fuels. We simply do not know. WebTAG, however, expects that everyone will still be travelling in the same way as now; only their numbers and the value of their time will have changed. Value at risk Value at risk models (VaR), used for risk management in banks, were the technique which lay behind Mr Viniar’s claim to have observed a ‘25 standard deviation’ event. These models were based on the portfolio theory pioneered by Markowitz and were developed at J. P. Morgan in the late 1980s to help the bank cope with the variety of debt instruments which had appeared in that decade.

INDEX 10 (film, 1979), 97 737 Max aircraft, 228 9/11 terror attacks, 7 , 74–6 , 202 , 230 Abbottabad raid (2011), 9–10 , 20 , 26 , 44 , 71 , 102 , 118–19 , 120 , 174–5 ; reference narrative of, 122–3 , 277 , 298 ; role of luck in, 262–3 ; and unhelpful probabilities, 8–19 , 326 abductive reasoning, 138 , 147 , 211 , 388 , 398 ABN AMRO, 257 Abraham (biblical character), 206 Abrahams, Harold, 273 Abramovich, Roman, 265 accountancy, 409 aeronautics, 227–8 , 352–6 , 383 Agdestein, Simen, 273 AIDS, 57 , 230 , 375–6 Airbus A380, 40 , 274–6 , 408 Akerlof, George, 250–1 , 252 , 253 , 254 , 382 Alchian, Armen, 158 alien invasion narratives, 295–6 Allais, Maurice, 134–5 , 136 , 137 , 437 , 440–3 Allen, Bill, 227–8 Allen, Paul, 28 , 29 Altair desktop, 28 Amazon, 289 , 309 Anderson, Roy, 375 ant colonies, 173 anthropology, 160 , 189–91 , 193–4 , 215–16 antibiotics, 40 , 45 , 284 , 429 Antz (film, 1998), 274 apocalyptic narratives, 331–2 , 335 , 358–62 Appiah, Anthony, 117–18 Apple, 29–30 , 31 , 169 , 309 Applegarth, Adam, 311 arbitrage, 308 Archilochus (Greek poet), 222 Aristotle, 137 , 147 , 303 Arrow, Kenneth, 254 , 343–5 , 440 artificial intelligence (AI), xvi , 39 , 135 , 150 , 173–4 , 175–6 , 185–6 , 387 ; the ‘singularity’, 176–7 Ashtabula rail bridge disaster (1876), 33 Asimov, Isaac, 303 asteroid strikes, 32 , 71–2 , 238 , 402 astrology, 394 astronomical laws, 18–19 , 35 , 70 , 373–4 , 388 , 389 , 391–2 , 394 AT&T, 28 auction theory, 255–7 Austen, Jane, 217 , 224–5 , 383 autism, 394 , 411 aviation, commercial, 23–4 , 40 , 227–8 , 274–6 , 315 , 383 , 414 axiomatic rationality: Allais disputes theory, 134–5 , 136 , 137 ; Arrow– Debreu world, 343–5 ; assumption of transitivity, 437 ; and Becker, 114 , 381–2 ; and behavioural economics, 116 , 135–6 , 141–9 , 154–5 , 167–8 , 386–7 , 401 ; capital asset pricing model (CAPM), 307–8 , 309 , 320 , 332 ; completeness axiom, 437–8 ; consistency of choice axiom, 108–9 , 110–11 ; continuity axiom, 438–40 ; definition of rationality, 133–4 , 137 , 436 ; definition of risk, 305 , 307 , 334 , 420–1 ; efficient market hypothesis, 252 , 254 , 308–9 , 318 , 320 , 332 , 336–7 ; efficient portfolio model, 307–8 , 309 , 318 , 320 , 332–4 , 366 ; and evolutionary rationality, 16 , 152–3 , 154–5 , 157 , 158 , 166–7 , 171–2 , 386–7 , 407 ; and ‘expectations’ concept, 97–8 , 102–3 , 121–2 , 341–2 ; extended to decision-making under uncertainty, xv , 40–2 , 110–14 , 133–7 , 257–9 , 420–1 ; and Friedman, 73–4 , 111–12 , 113–14 , 125 , 257–9 , 307 , 399–400 , 420 , 437 ; hegemony of over radical uncertainty, 40–2 , 110–14 ; implausibility of assumptions, xiv–xv , 16 , 41–4 , 47 , 74–84 , 85–105 , 107–9 , 111 , 116–22 , 344–9 , 435–44 ; independence axiom, 440–4 ; as limited to small worlds, 170 , 309–10 , 320–1 , 342–9 , 382 , 400 , 421 ; and Lucas, 36 , 92 , 93 , 338–9 , 341 , 345 , 346 ; and Markowitz, 307 , 308 , 309–10 , 318 , 322 , 333 ; maximising behaviour, 310 ; ‘pignistic probability’, 78–84 , 438 ; and Popperian falsificationism, 259–60 ; Prescott’s comparison with engineering, 352–6 ; ‘rational expectations theory, 342–5 , 346–50 ; and Samuelson, xv , 42 , 110–11 , 436 ; and Savage, 111–14 , 125 , 257–9 , 309 , 345 , 400 , 435 , 437 , 442–3 ; shocks and shifts discourse, 42 , 346 , 347 , 348 , 406–7 ; Simon’s work on, 134 , 136 , 149–53 ; triumph of probabilistic reasoning, 15–16 , 20 , 72–84 , 110–14 ; Value at risk models (VaR), 366–8 , 405 , 424 ; von Neumann–Morgenstern axioms, 111 , 133 , 435–44 ; see also maximising behaviour Ballmer, Steve, 30 , 227 Bank of England, xiii , 45 , 103–5 , 286 , 311 Barclays Bank, 257 Barings Bank, 411 Basel regulations, 310 , 311 Bay of Pigs fiasco (1961), 278–9 Bayes, Reverend Thomas, 60–3 , 66–7 , 70 , 71 , 358 , 431 Beane, Billy, 273 Bear Stearns, 158–9 Becker, Gary, 114 , 381–2 Beckham, David, 267–8 , 269 , 270 , 272–3 , 414 behavioural economics, 116 , 145–8 , 154 , 386–7 ; and Allais paradox, 442 ; ‘availability heuristic’, 144–5 ; biases in human behaviour, 16 , 136 , 141–8 , 154 , 162 , 165 , 167–8 , 170–1 , 175–6 , 184 , 401 ; and evolutionary science, 154–5 , 165 ; Kahneman’s dual systems, 170–1 , 172 , 271 ; Kahneman–Tversky experiments, 141–7 , 152 , 215 ; ‘noise’ (randomness), 175–6 ; nudge theory, 148–9 Bentham, Jeremy, 110 Berkshire Hathaway, 153 , 319 , 324 , 325–6 Berlin, Isaiah, 222 Bernoulli, Daniel, 114–16 , 199 Bernoulli, Nicolaus, 199 , 442 Bertrand, Joseph, 70 Bezos, Jeff, 289 big data, 208 , 327 , 388–90 billiard players, 257–8 bin Laden, Osama, 7 , 8–10 , 21 , 44 , 71 , 118–19 , 120 , 122–3 , 262–3 , 326 Bismarck, Otto von, 161 Bitcoin, 96 , 316 Black Death, 32 , 39–40 BlackBerry, 30 , 31 blackjack, 38 Blackstone, Sir William, 213 BNP Paribas, 5 , 6 BOAC, 23–4 Boas, Franz, 193 Boeing, 24 , 227–8 Boer War, 168 Bolt, Usain, 273 bonobos, 161–2 , 178 Borges, Jorge Luis, 391 Borodino, battle of (1812), 3–4 , 433 Bortkiewicz, Ladislaus, 235–6 Bower, Tom, 169–70 Bowral cricket team, New South Wales, 264 Box, George, 393 Boycott, Geoffrey, 264–5 Bradman, Don, 237 , 264 Brahe, Tycho, 388–9 Brånemark, Per-Ingvar, 387 , 388 Branson, Richard, 169–70 Brearley, Michael, 140–1 , 264–5 Breslau (now Wrocław), 56 Brexit referendum (June 2016), 241–2 ; lies told during, 404 bridge collapses, 33 , 341 Brownian motion, 37 Brunelleschi, Filippo, 143 , 147 Buffett, Warren, 83 , 152 , 179 , 319–20 , 324 , 335 , 336–7 Burns, Robert, 253 Bush, George W., 295 , 407 , 412 business cycles, 347 business history (academic discipline), 286 business schools, 318 business strategy: approach in 1970s, 183 ; approach in 1980s, 181–2 ; aspirations confused with, 181–2 , 183–4 ; business plans, 223–4 , 228 ; collections of capabilities, 274–7 ; and the computer industry, 27–31 ; corporate takeovers, 256–7 ; Lampert at Sears, 287–9 , 292 ; Henry Mintzberg on, 296 , 410 ; motivational proselytisation, 182–3 , 184 ; quantification mistaken for understanding, 180–1 , 183 ; and reference narratives, 286–90 , 296–7 ; risk maps, 297 ; Rumelt’s MBA classes, 10 , 178–80 ; Shell’s scenario planning, 223 , 295 ; Sloan at General Motors, 286–7 ; strategy weekends, 180–3 , 194 , 296 , 407 ; three common errors, 183–4 ; vision or mission statements, 181–2 , 184 Buxton, Jedediah, 225 Calas, Jean, 199 California, 48–9 Cambridge Growth Project, 340 Canadian fishing industry, 368–9 , 370 , 423 , 424 cancer, screening for, 66–7 Candler, Graham, 352 , 353–6 , 399 Cardiff City Football Club, 265 Carlsen, Magnus, 175 , 273 Carnegie, Andrew, 427 Carnegie Mellon University, 135 Carré, Dr Matt, 267–8 Carroll, Lewis, Through the Looking-Glass , 93–4 , 218 , 344 , 346 ; ‘Jabberwocky’, 91–2 , 94 , 217 Carron works (near Falkirk), 253 Carter, Jimmy, 8 , 119 , 120 , 123 , 262–3 cartography, 391 Casio, 27 , 31 Castro, Fidel, 278–9 cave paintings, 216 central banks, 5 , 7 , 95 , 96 , 103–5 , 285–6 , 348–9 , 350 , 351 , 356–7 Central Pacific Railroad, 48 Centre for the Study of Existential Risk, 39 Chabris, Christopher, 140 Challenger disaster (1986), 373 , 374 Chamberlain, Neville, 24–5 Chandler, Alfred, Strategy and Structure , 286 Chariots of Fire (film, 1981), 273 Charles II, King, 383 Chelsea Football Club, 265 chess, 173 , 174 , 175 , 266 , 273 , 346 Chicago economists, 36 , 72–4 , 86 , 92 , 111–14 , 133–7 , 158 , 257–8 , 307 , 342–3 , 381–2 Chicago Mercantile Exchange, 423 chimpanzees, 161–2 , 178 , 274 China, 4–5 , 419–20 , 430 cholera, 283 Churchill, Winston: character of, 25–6 , 168 , 169 , 170 ; fondness for gambling, 81 , 168 ; as hedgehog not fox, 222 ; on Montgomery, 293 ; restores gold standard (1925), 25–6 , 269 ; The Second World War , 187 ; Second World War leadership, 24–5 , 26 , 119 , 167 , 168–9 , 170 , 184 , 187 , 266 , 269 Citibank, 255 Civil War, American, 188 , 266 , 290 Clapham, John, 253 Clark, Sally, 197–8 , 200 , 202 , 204 , 206 Clausewitz, Carl von, On War , 433 climate systems, 101–2 Club of Rome, 361 , 362 Coase, Ronald, 286 , 342 Cochran, Johnnie, 198 , 217 Cochrane, John, 93 coffee houses, 55–6 cognitive illusions, 141–2 Cohen, Jonathan, 206–7 Colbert, Jean-Baptiste, 411 Cold War, 293–4 , 306–7 Collier, Paul, 276–7 Columbia disaster (2003), 373 Columbia University, 117 , 118 , 120 Columbus, Christopher, 4 , 21 Colyvan, Mark, 225 Comet aircraft, 23–4 , 228 communication: communicative rationality, 172 , 267–77 , 279–82 , 412 , 414–16 ; and decision-making, 17 , 231 , 272–7 , 279–82 , 398–9 , 408 , 412 , 413–17 , 432 ; eusociality, 172–3 , 274 ; and good doctors, 185 , 398–9 ; human capacity for, 159 , 161 , 162 , 172–3 , 216 , 272–7 , 408 ; and ill-defined concepts, 98–9 ; and intelligibility, 98 ; language, 98 , 99–100 , 159 , 162 , 173 , 226 ; linguistic ambiguity, 98–100 ; and reasoning, 265–8 , 269–77 ; and the smartphone, 30 ; the ‘wisdom of crowds’, 47 , 413–14 Community Reinvestment Act (USA, 1977), 207 comparative advantage model, 249–50 , 251–2 , 253 computer technologies, 27–31 , 173–4 , 175–7 , 185–6 , 227 , 411 ; big data, 208 , 327 , 388–90 ; CAPTCHA text, 387 ; dotcom boom, 228 ; and economic models, 339–40 ; machine learning, 208 Condit, Phil, 228 Condorcet, Nicolas de, 199–200 consumer price index, 330 , 331 conviction narrative theory, 227–30 Corinthians (New Testament), 402 corporate takeovers, 256–7 corporations, large, 27–31 , 122 , 123 , 286–90 , 408–10 , 412 , 415 Cosmides, Leda, 165 Cretaceous–Paleogene extinction, 32 , 39 , 71–2 Crick, Francis, 156 cricket, 140–1 , 237 , 263–5 crime novels, classic, 218 crosswords, 218 crypto-currencies, 96 , 316 Csikszentmihalyi, Mihaly, 140 , 264 Cuba, 278–80 ; Cuban Missile Crisis, 279–81 , 299 , 412 Custer, George, 293 Cutty Sark (whisky producer), 325 Daily Express , 242–3 , 244 Damasio, Antonio, 171 Dardanelles expedition (1915), 25 Darwin, Charles, 156 , 157 Davenport, Thomas, 374 Dawkins, Richard, 156 de Havilland company, 23–4 Debreu, Gerard, 254 , 343–4 decision theory, xvi ; critiques of ‘American school’, 133–7 ; definition of rationality, 133–4 ; derived from deductive reasoning, 138 ; Ellsberg’s ‘ambiguity aversion’, 135 ; expected utility , 111–14 , 115–18 , 124–5 , 127 , 128 – 30 , 135 , 400 , 435–44 ; hegemony of optimisation, 40–2 , 110–14 ; as unable to solve mysteries, 34 , 44 , 47 ; and work of Savage, 442–3 decision-making under uncertainty: and adaptation, 102 , 401 ; Allais paradox, 133–7 , 437 , 440–3 ; axiomatic approach extended to, xv , 40–2 , 110–14 , 133–7 , 257–9 , 420–1 ; ‘bounded rationality concept, 149–53 ; as collaborative process, 17 , 155 , 162 , 176 , 411–15 , 431–2 ; and communication, 17 , 231 , 272–7 , 279–82 , 398–9 , 408 , 412 , 413–17 , 432 ; communicative rationality, 172 , 267–77 , 279–82 , 412 , 414–16 ; completeness axiom, 437–8 ; continuity axiom, 438–40 ; Cuban Missile Crisis, 279–81 , 299 , 412 ; ‘decision weights’ concept, 121 ; disasters attributed to chance, 266–7 ; doctors, 184–6 , 194 , 398–9 ; and emotions, 227–9 , 411 ; ‘evidence-based policy’, 404 , 405 ; excessive attention to prior probabilities, 184–5 , 210 ; expected utility , 111–14 , 115–18 , 124–5 , 127 , 128–30 , 135 , 400 , 435–44 ; first-rate decision-makers, 285 ; framing of problems, 261 , 362 , 398–400 ; good strategies for radical uncertainty, 423–5 ; and hindsight, 263 ; independence axiom, 440–4 ; judgement as unavoidable, 176 ; Klein’s ‘primed recognition decision-making’, 399 ; Gary Klein’s work on, 151–2 , 167 ; and luck, 263–6 ; practical decision-making, 22–6 , 46–7 , 48–9 , 81–2 , 151 , 171–2 , 176–7 , 255 , 332 , 383 , 395–6 , 398–9 ; and practical knowledge, 22–6 , 195 , 255 , 352 , 382–8 , 395–6 , 405 , 414–15 , 431 ; and prior opinions, 179–80 , 184–5 , 210 ; ‘prospect theory’, 121 ; public sector processes, 183 , 355 , 415 ; puzzle– mystery distinction, 20–4 , 32–4 , 48–9 , 64–8 , 100 , 155 , 173–7 , 218 , 249 , 398 , 400–1 ; qualities needed for success, 179–80 ; reasoning as not decision-making, 268–71 ; and ‘resulting’, 265–7 ; ‘risk as feelings’ perspective, 128–9 , 310 ; robustness and resilience, 123 , 294–8 , 332 , 335 , 374 , 423–5 ; and role of economists, 397–401 ; Rumelt’s ‘diagnosis’, 184–5 , 194–5 ; ‘satisficing’ (’good enough’ outcomes), 150 , 167 , 175 , 415 , 416 ; search for a workable solution, 151–2 , 167 ; by securities traders, 268–9 ; ‘shock’ and ‘shift’ labels, 42 , 346 , 347 , 348 , 406–7 ; simple heuristics, rules of thumb, 152 ; and statistical discrimination, 207–9 , 415 ; triumph of probabilistic reasoning, 20 , 40–2 , 72–84 , 110–14 ; von Neumann– Morgenstern axioms, 111 , 133 , 435–44 ; see also business strategy deductive reasoning, 137–8 , 147 , 235 , 388 , 389 , 398 Deep Blue, 175 DeepMind, 173–4 The Deer Hunter (film, 1978), 438 democracy, representative, 292 , 319 , 414 demographic issues, 253 , 358–61 , 362–3 ; EU migration models, 369–70 , 372 Denmark, 426 , 427 , 428 , 430 dentistry, 387–8 , 394 Derek, Bo, 97 dermatologists, 88–9 Digital Equipment Corporation (DEC), 27 , 31 dinosaurs, extinction of, 32 , 39 , 71–2 , 383 , 402 division of labour, 161 , 162 , 172–3 , 216 , 249 DNA, 156 , 198 , 201 , 204 ‘domino theory’, 281 Donoghue, Denis, 226 dotcom boom, 316 , 402 Doyle, Arthur Conan, 34 , 224–5 , 253 Drapers Company, 328 Drescher, Melvin, 248–9 Drucker, Peter, Concept of the Corporation (1946), 286 , 287 Duhem–Quine hypothesis, 259–60 Duke, Annie, 263 , 268 , 273 Dulles, John Foster, 293 Dutch tulip craze (1630s), 315 Dyson, Frank, 259 earthquakes, 237–8 , 239 Eco, Umberto, The Name of the Rose , 204 Econometrica , 134 econometrics, 134 , 340–1 , 346 , 356 economic models: of 1950s and 1960s, 339–40 ; Akerlof model, 250–1 , 252 , 253 , 254 ; ‘analogue economies’ of Lucas, 345 , 346 ; artificial/complex, xiv–xv , 21 , 92–3 , 94 ; ‘asymmetric information’ model, 250–1 , 254–5 ; capital asset pricing model (CAPM), 307–8 , 309 , 320 , 332 ; comparative advantage model, 249–50 , 251–2 , 253 ; cost-benefit analysis obsession, 404 ; diversification of risk, 304–5 , 307–9 , 317–18 , 334–7 ; econometric models, 340–1 , 346 , 356 ; economic rent model, 253–4 ; efficient market hypothesis, 252 , 254 , 308–9 , 318 , 320 , 332 , 336–7 ; efficient portfolio model, 307–8 , 309 , 318 , 320 , 332–4 , 366 ; failure over 2007–08 crisis, xv , 6–7 , 260 , 311–12 , 319 , 339 , 349–50 , 357 , 367–8 , 399 , 407 , 423–4 ; falsificationist argument, 259–60 ; forecasting models, 7 , 15–16 , 68 , 96 , 102–5 , 347–50 , 403–4 ; Goldman Sachs risk models, 6–7 , 9 , 68 , 202 , 246–7 ; ‘grand auction’ of Arrow and Debreu, 343–5 ; inadequacy of forecasting models, 347–50 , 353–4 , 403–4 ; invented numbers in, 312–13 , 320 , 363–4 , 365 , 371 , 373 , 404 , 405 , 423 ; Keynesian, 339–40 ; Lucas critique, 341 , 348 , 354 ; Malthus’ population growth model, 253 , 358–61 , 362–3 ; misuse/abuse of, 312–13 , 320 , 371–4 , 405 ; need for, 404–5 ; need for pluralism of, 276–7 ; pension models, 312–13 , 328–9 , 405 , 423 , 424 ; pre-crisis risk models, 6–7 , 9 , 68 , 202 , 246–7 , 260 , 311–12 , 319 , 320–1 , 339 ; purpose of, 346 ; quest for large-world model, 392 ; ‘rational expectations theory, 342–5 , 346–50 ; real business cycle theory, 348 , 352–4 ; role of incentives, 408–9 ; ‘shift’ label, 406–7 ; ‘shock’ label, 346–7 , 348 , 406–7 ; ‘training base’ (historical data series), 406 ; Value at risk models (VaR), 366–8 , 405 , 424 ; Viniar problem (problem of model failure), 6–7 , 58 , 68 , 109 , 150 , 176 , 202 , 241 , 242 , 246–7 , 331 , 366–8 ; ‘wind tunnel’ models, 309 , 339 , 392 ; winner’s curse model, 256–7 ; World Economic Outlook, 349 ; see also axiomatic rationality; maximising behaviour; optimising behaviour; small world models Economic Policy Symposium, Jackson Hole, 317–18 economics: adverse selection process, 250–1 , 327 ; aggregate output and GDP, 95 ; ambiguity of variables/concepts, 95–6 , 99–100 ; appeal of probability theory, 42–3 ; ‘bubbles’, 315–16 ; business cycles, 45–6 , 347 ; Chicago School, 36 , 72–4 , 86 , 92 , 111–14 , 133–7 , 158 , 257–8 , 307 , 342–3 , 381–2 ; data as essential, 388–90 ; division of labour, 161 , 162 , 172–3 , 216 , 249 ; and evolutionary mechanisms, 158–9 ; ‘expectations’ concept, 97–8 , 102–3 , 121–2 , 341–2 ; forecasts and future planning as necessary, 103 ; framing of problems, 261 , 362 , 398–400 ; ‘grand auction’ of Arrow and Debreu, 343–5 ; hegemony of optimisation, 40–2 , 110 – 14 ; Hicks–Samuelson axioms, 435–6 ; market fundamentalism, 220 ; market price equilibrium, 254 , 343–4 , 381–2 ; markets as necessarily incomplete, 344 , 345 , 349 ; Marshall’s definition of, 381 , 382 ; as ‘non-stationary’, 16 , 35–6 , 45–6 , 102 , 236 , 339–41 , 349 , 350 , 394–6 ; oil shock (1973), 223 ; Phillips curve, 340 ; and ‘physics envy’, 387 , 388 ; and power laws, 238–9 ; as practical knowledge, 381 , 382–3 , 385–8 , 398 , 399 , 405 ; public role of the social scientist, 397–401 ; reciprocity in a modern economy, 191–2 , 328–9 ; and reflexivity, 35–6 , 309 , 394 ; risk and volatility, 124–5 , 310 , 333 , 335–6 , 421–3 ; Romer’s ‘mathiness’, 93–4 , 95 ; shift or structural break, 236 ; Adam Smith’s ‘invisible hand’, 163 , 254 , 343 ; social context of, 17 ; sources of data, 389 , 390 ; surge in national income since 1800, 161 ; systems as non-linear, 102 ; teaching’s emphasis on quantitative methods, 389 ; validity of research findings, 245 ‘Economists Free Ride, Does Anyone Else?’

., 295 , 407 , 412 business cycles, 347 business history (academic discipline), 286 business schools, 318 business strategy: approach in 1970s, 183 ; approach in 1980s, 181–2 ; aspirations confused with, 181–2 , 183–4 ; business plans, 223–4 , 228 ; collections of capabilities, 274–7 ; and the computer industry, 27–31 ; corporate takeovers, 256–7 ; Lampert at Sears, 287–9 , 292 ; Henry Mintzberg on, 296 , 410 ; motivational proselytisation, 182–3 , 184 ; quantification mistaken for understanding, 180–1 , 183 ; and reference narratives, 286–90 , 296–7 ; risk maps, 297 ; Rumelt’s MBA classes, 10 , 178–80 ; Shell’s scenario planning, 223 , 295 ; Sloan at General Motors, 286–7 ; strategy weekends, 180–3 , 194 , 296 , 407 ; three common errors, 183–4 ; vision or mission statements, 181–2 , 184 Buxton, Jedediah, 225 Calas, Jean, 199 California, 48–9 Cambridge Growth Project, 340 Canadian fishing industry, 368–9 , 370 , 423 , 424 cancer, screening for, 66–7 Candler, Graham, 352 , 353–6 , 399 Cardiff City Football Club, 265 Carlsen, Magnus, 175 , 273 Carnegie, Andrew, 427 Carnegie Mellon University, 135 Carré, Dr Matt, 267–8 Carroll, Lewis, Through the Looking-Glass , 93–4 , 218 , 344 , 346 ; ‘Jabberwocky’, 91–2 , 94 , 217 Carron works (near Falkirk), 253 Carter, Jimmy, 8 , 119 , 120 , 123 , 262–3 cartography, 391 Casio, 27 , 31 Castro, Fidel, 278–9 cave paintings, 216 central banks, 5 , 7 , 95 , 96 , 103–5 , 285–6 , 348–9 , 350 , 351 , 356–7 Central Pacific Railroad, 48 Centre for the Study of Existential Risk, 39 Chabris, Christopher, 140 Challenger disaster (1986), 373 , 374 Chamberlain, Neville, 24–5 Chandler, Alfred, Strategy and Structure , 286 Chariots of Fire (film, 1981), 273 Charles II, King, 383 Chelsea Football Club, 265 chess, 173 , 174 , 175 , 266 , 273 , 346 Chicago economists, 36 , 72–4 , 86 , 92 , 111–14 , 133–7 , 158 , 257–8 , 307 , 342–3 , 381–2 Chicago Mercantile Exchange, 423 chimpanzees, 161–2 , 178 , 274 China, 4–5 , 419–20 , 430 cholera, 283 Churchill, Winston: character of, 25–6 , 168 , 169 , 170 ; fondness for gambling, 81 , 168 ; as hedgehog not fox, 222 ; on Montgomery, 293 ; restores gold standard (1925), 25–6 , 269 ; The Second World War , 187 ; Second World War leadership, 24–5 , 26 , 119 , 167 , 168–9 , 170 , 184 , 187 , 266 , 269 Citibank, 255 Civil War, American, 188 , 266 , 290 Clapham, John, 253 Clark, Sally, 197–8 , 200 , 202 , 204 , 206 Clausewitz, Carl von, On War , 433 climate systems, 101–2 Club of Rome, 361 , 362 Coase, Ronald, 286 , 342 Cochran, Johnnie, 198 , 217 Cochrane, John, 93 coffee houses, 55–6 cognitive illusions, 141–2 Cohen, Jonathan, 206–7 Colbert, Jean-Baptiste, 411 Cold War, 293–4 , 306–7 Collier, Paul, 276–7 Columbia disaster (2003), 373 Columbia University, 117 , 118 , 120 Columbus, Christopher, 4 , 21 Colyvan, Mark, 225 Comet aircraft, 23–4 , 228 communication: communicative rationality, 172 , 267–77 , 279–82 , 412 , 414–16 ; and decision-making, 17 , 231 , 272–7 , 279–82 , 398–9 , 408 , 412 , 413–17 , 432 ; eusociality, 172–3 , 274 ; and good doctors, 185 , 398–9 ; human capacity for, 159 , 161 , 162 , 172–3 , 216 , 272–7 , 408 ; and ill-defined concepts, 98–9 ; and intelligibility, 98 ; language, 98 , 99–100 , 159 , 162 , 173 , 226 ; linguistic ambiguity, 98–100 ; and reasoning, 265–8 , 269–77 ; and the smartphone, 30 ; the ‘wisdom of crowds’, 47 , 413–14 Community Reinvestment Act (USA, 1977), 207 comparative advantage model, 249–50 , 251–2 , 253 computer technologies, 27–31 , 173–4 , 175–7 , 185–6 , 227 , 411 ; big data, 208 , 327 , 388–90 ; CAPTCHA text, 387 ; dotcom boom, 228 ; and economic models, 339–40 ; machine learning, 208 Condit, Phil, 228 Condorcet, Nicolas de, 199–200 consumer price index, 330 , 331 conviction narrative theory, 227–30 Corinthians (New Testament), 402 corporate takeovers, 256–7 corporations, large, 27–31 , 122 , 123 , 286–90 , 408–10 , 412 , 415 Cosmides, Leda, 165 Cretaceous–Paleogene extinction, 32 , 39 , 71–2 Crick, Francis, 156 cricket, 140–1 , 237 , 263–5 crime novels, classic, 218 crosswords, 218 crypto-currencies, 96 , 316 Csikszentmihalyi, Mihaly, 140 , 264 Cuba, 278–80 ; Cuban Missile Crisis, 279–81 , 299 , 412 Custer, George, 293 Cutty Sark (whisky producer), 325 Daily Express , 242–3 , 244 Damasio, Antonio, 171 Dardanelles expedition (1915), 25 Darwin, Charles, 156 , 157 Davenport, Thomas, 374 Dawkins, Richard, 156 de Havilland company, 23–4 Debreu, Gerard, 254 , 343–4 decision theory, xvi ; critiques of ‘American school’, 133–7 ; definition of rationality, 133–4 ; derived from deductive reasoning, 138 ; Ellsberg’s ‘ambiguity aversion’, 135 ; expected utility , 111–14 , 115–18 , 124–5 , 127 , 128 – 30 , 135 , 400 , 435–44 ; hegemony of optimisation, 40–2 , 110–14 ; as unable to solve mysteries, 34 , 44 , 47 ; and work of Savage, 442–3 decision-making under uncertainty: and adaptation, 102 , 401 ; Allais paradox, 133–7 , 437 , 440–3 ; axiomatic approach extended to, xv , 40–2 , 110–14 , 133–7 , 257–9 , 420–1 ; ‘bounded rationality concept, 149–53 ; as collaborative process, 17 , 155 , 162 , 176 , 411–15 , 431–2 ; and communication, 17 , 231 , 272–7 , 279–82 , 398–9 , 408 , 412 , 413–17 , 432 ; communicative rationality, 172 , 267–77 , 279–82 , 412 , 414–16 ; completeness axiom, 437–8 ; continuity axiom, 438–40 ; Cuban Missile Crisis, 279–81 , 299 , 412 ; ‘decision weights’ concept, 121 ; disasters attributed to chance, 266–7 ; doctors, 184–6 , 194 , 398–9 ; and emotions, 227–9 , 411 ; ‘evidence-based policy’, 404 , 405 ; excessive attention to prior probabilities, 184–5 , 210 ; expected utility , 111–14 , 115–18 , 124–5 , 127 , 128–30 , 135 , 400 , 435–44 ; first-rate decision-makers, 285 ; framing of problems, 261 , 362 , 398–400 ; good strategies for radical uncertainty, 423–5 ; and hindsight, 263 ; independence axiom, 440–4 ; judgement as unavoidable, 176 ; Klein’s ‘primed recognition decision-making’, 399 ; Gary Klein’s work on, 151–2 , 167 ; and luck, 263–6 ; practical decision-making, 22–6 , 46–7 , 48–9 , 81–2 , 151 , 171–2 , 176–7 , 255 , 332 , 383 , 395–6 , 398–9 ; and practical knowledge, 22–6 , 195 , 255 , 352 , 382–8 , 395–6 , 405 , 414–15 , 431 ; and prior opinions, 179–80 , 184–5 , 210 ; ‘prospect theory’, 121 ; public sector processes, 183 , 355 , 415 ; puzzle– mystery distinction, 20–4 , 32–4 , 48–9 , 64–8 , 100 , 155 , 173–7 , 218 , 249 , 398 , 400–1 ; qualities needed for success, 179–80 ; reasoning as not decision-making, 268–71 ; and ‘resulting’, 265–7 ; ‘risk as feelings’ perspective, 128–9 , 310 ; robustness and resilience, 123 , 294–8 , 332 , 335 , 374 , 423–5 ; and role of economists, 397–401 ; Rumelt’s ‘diagnosis’, 184–5 , 194–5 ; ‘satisficing’ (’good enough’ outcomes), 150 , 167 , 175 , 415 , 416 ; search for a workable solution, 151–2 , 167 ; by securities traders, 268–9 ; ‘shock’ and ‘shift’ labels, 42 , 346 , 347 , 348 , 406–7 ; simple heuristics, rules of thumb, 152 ; and statistical discrimination, 207–9 , 415 ; triumph of probabilistic reasoning, 20 , 40–2 , 72–84 , 110–14 ; von Neumann– Morgenstern axioms, 111 , 133 , 435–44 ; see also business strategy deductive reasoning, 137–8 , 147 , 235 , 388 , 389 , 398 Deep Blue, 175 DeepMind, 173–4 The Deer Hunter (film, 1978), 438 democracy, representative, 292 , 319 , 414 demographic issues, 253 , 358–61 , 362–3 ; EU migration models, 369–70 , 372 Denmark, 426 , 427 , 428 , 430 dentistry, 387–8 , 394 Derek, Bo, 97 dermatologists, 88–9 Digital Equipment Corporation (DEC), 27 , 31 dinosaurs, extinction of, 32 , 39 , 71–2 , 383 , 402 division of labour, 161 , 162 , 172–3 , 216 , 249 DNA, 156 , 198 , 201 , 204 ‘domino theory’, 281 Donoghue, Denis, 226 dotcom boom, 316 , 402 Doyle, Arthur Conan, 34 , 224–5 , 253 Drapers Company, 328 Drescher, Melvin, 248–9 Drucker, Peter, Concept of the Corporation (1946), 286 , 287 Duhem–Quine hypothesis, 259–60 Duke, Annie, 263 , 268 , 273 Dulles, John Foster, 293 Dutch tulip craze (1630s), 315 Dyson, Frank, 259 earthquakes, 237–8 , 239 Eco, Umberto, The Name of the Rose , 204 Econometrica , 134 econometrics, 134 , 340–1 , 346 , 356 economic models: of 1950s and 1960s, 339–40 ; Akerlof model, 250–1 , 252 , 253 , 254 ; ‘analogue economies’ of Lucas, 345 , 346 ; artificial/complex, xiv–xv , 21 , 92–3 , 94 ; ‘asymmetric information’ model, 250–1 , 254–5 ; capital asset pricing model (CAPM), 307–8 , 309 , 320 , 332 ; comparative advantage model, 249–50 , 251–2 , 253 ; cost-benefit analysis obsession, 404 ; diversification of risk, 304–5 , 307–9 , 317–18 , 334–7 ; econometric models, 340–1 , 346 , 356 ; economic rent model, 253–4 ; efficient market hypothesis, 252 , 254 , 308–9 , 318 , 320 , 332 , 336–7 ; efficient portfolio model, 307–8 , 309 , 318 , 320 , 332–4 , 366 ; failure over 2007–08 crisis, xv , 6–7 , 260 , 311–12 , 319 , 339 , 349–50 , 357 , 367–8 , 399 , 407 , 423–4 ; falsificationist argument, 259–60 ; forecasting models, 7 , 15–16 , 68 , 96 , 102–5 , 347–50 , 403–4 ; Goldman Sachs risk models, 6–7 , 9 , 68 , 202 , 246–7 ; ‘grand auction’ of Arrow and Debreu, 343–5 ; inadequacy of forecasting models, 347–50 , 353–4 , 403–4 ; invented numbers in, 312–13 , 320 , 363–4 , 365 , 371 , 373 , 404 , 405 , 423 ; Keynesian, 339–40 ; Lucas critique, 341 , 348 , 354 ; Malthus’ population growth model, 253 , 358–61 , 362–3 ; misuse/abuse of, 312–13 , 320 , 371–4 , 405 ; need for, 404–5 ; need for pluralism of, 276–7 ; pension models, 312–13 , 328–9 , 405 , 423 , 424 ; pre-crisis risk models, 6–7 , 9 , 68 , 202 , 246–7 , 260 , 311–12 , 319 , 320–1 , 339 ; purpose of, 346 ; quest for large-world model, 392 ; ‘rational expectations theory, 342–5 , 346–50 ; real business cycle theory, 348 , 352–4 ; role of incentives, 408–9 ; ‘shift’ label, 406–7 ; ‘shock’ label, 346–7 , 348 , 406–7 ; ‘training base’ (historical data series), 406 ; Value at risk models (VaR), 366–8 , 405 , 424 ; Viniar problem (problem of model failure), 6–7 , 58 , 68 , 109 , 150 , 176 , 202 , 241 , 242 , 246–7 , 331 , 366–8 ; ‘wind tunnel’ models, 309 , 339 , 392 ; winner’s curse model, 256–7 ; World Economic Outlook, 349 ; see also axiomatic rationality; maximising behaviour; optimising behaviour; small world models Economic Policy Symposium, Jackson Hole, 317–18 economics: adverse selection process, 250–1 , 327 ; aggregate output and GDP, 95 ; ambiguity of variables/concepts, 95–6 , 99–100 ; appeal of probability theory, 42–3 ; ‘bubbles’, 315–16 ; business cycles, 45–6 , 347 ; Chicago School, 36 , 72–4 , 86 , 92 , 111–14 , 133–7 , 158 , 257–8 , 307 , 342–3 , 381–2 ; data as essential, 388–90 ; division of labour, 161 , 162 , 172–3 , 216 , 249 ; and evolutionary mechanisms, 158–9 ; ‘expectations’ concept, 97–8 , 102–3 , 121–2 , 341–2 ; forecasts and future planning as necessary, 103 ; framing of problems, 261 , 362 , 398–400 ; ‘grand auction’ of Arrow and Debreu, 343–5 ; hegemony of optimisation, 40–2 , 110 – 14 ; Hicks–Samuelson axioms, 435–6 ; market fundamentalism, 220 ; market price equilibrium, 254 , 343–4 , 381–2 ; markets as necessarily incomplete, 344 , 345 , 349 ; Marshall’s definition of, 381 , 382 ; as ‘non-stationary’, 16 , 35–6 , 45–6 , 102 , 236 , 339–41 , 349 , 350 , 394–6 ; oil shock (1973), 223 ; Phillips curve, 340 ; and ‘physics envy’, 387 , 388 ; and power laws, 238–9 ; as practical knowledge, 381 , 382–3 , 385–8 , 398 , 399 , 405 ; public role of the social scientist, 397–401 ; reciprocity in a modern economy, 191–2 , 328–9 ; and reflexivity, 35–6 , 309 , 394 ; risk and volatility, 124–5 , 310 , 333 , 335–6 , 421–3 ; Romer’s ‘mathiness’, 93–4 , 95 ; shift or structural break, 236 ; Adam Smith’s ‘invisible hand’, 163 , 254 , 343 ; social context of, 17 ; sources of data, 389 , 390 ; surge in national income since 1800, 161 ; systems as non-linear, 102 ; teaching’s emphasis on quantitative methods, 389 ; validity of research findings, 245 ‘Economists Free Ride, Does Anyone Else?’


pages: 225 words: 11,355

Financial Market Meltdown: Everything You Need to Know to Understand and Survive the Global Credit Crisis by Kevin Mellyn

Alan Greenspan, asset-backed security, bank run, banking crisis, Bernie Madoff, bond market vigilante , bonus culture, Bretton Woods, business cycle, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, deal flow, disintermediation, diversification, fiat currency, financial deregulation, financial engineering, financial innovation, financial intermediation, fixed income, foreign exchange controls, Francis Fukuyama: the end of history, George Santayana, global reserve currency, Greenspan put, Home mortgage interest deduction, inverted yield curve, Isaac Newton, joint-stock company, junk bonds, Kickstarter, liquidity trap, London Interbank Offered Rate, long peace, low interest rates, margin call, market clearing, mass immigration, Money creation, money market fund, moral hazard, mortgage tax deduction, Nixon triggered the end of the Bretton Woods system, Northern Rock, offshore financial centre, paradox of thrift, pattern recognition, pension reform, pets.com, Phillips curve, plutocrats, Ponzi scheme, profit maximization, proprietary trading, pushing on a string, reserve currency, risk tolerance, risk-adjusted returns, road to serfdom, Ronald Reagan, shareholder value, Silicon Valley, South Sea Bubble, statistical model, Suez canal 1869, systems thinking, tail risk, The Great Moderation, the long tail, the new new thing, the payments system, too big to fail, value at risk, very high income, War on Poverty, We are all Keynesians now, Y2K, yield curve

Banks became seized with a superstitious belief that complex mathematical models could better manage financial risk and return than human judgment. This thinking went well beyond the FICO score or the models used by the rating agencies to ‘‘stress test’’ default probabilities. Banks came to believe that they could design and implement data-driven ‘‘scientific’’ risk systems. The key concepts were ‘‘value at risk’’ or VAR and ‘‘risk adjusted return on capital’’ or RAROC. The basic idea was simple. Every loan, trading position, or operating exposure such as fraud or computer systems failure involved risks that Financial Innovation Made Easy could be identified and quantified with some precision across the whole institution.

Banks and investment banks spent tens of millions of dollars on computer systems that allowed the exposure to risk of every line of business, down to loan portfolios and trading positions, to be calculated. Many banks were capable of producing daily reports that summed up the value at risk of the entire institution on a daily basis. These VAR reports were reviewed by top management and taken seriously by them and the risk management committees of their boards. Regulators, including the Bank for International Settlements, which represented the central banks of the world’s advanced economies, endorsed this approach to risk.

See Stocks silver, xv, xvi, 8, 34, 83, 95 197 198 Index Sixteenth Amendment (to US constitution), 181 Smith, Adam, 179–180 Social Security, 23, 157 Socialism, 124–126, 182–183, 188–189 South Sea Bubble, 137 sovereign immunity, 151 sovereign lending, 151–152 speculation, 53, 109, 132, 138 Spitzer, Eliot, 138 stimulus and crisis management in US, Japan, 114, 169, 172 stocks, x–xi, xix, 3, 7, 13, 20, 22, 25, 27, 42, 49, 50–55, 60, 70–73, 80, 87, 137, 139, 142, 165, 167–168, 188; defined, 46; in Great Depression, 109–110; stock exchange, 88–89; stock prices, 47; versus bonds, 48; why stocks are risky, 47 Strong, Benjamin (‘‘Ben Strong’’), 105–106, 108–111 ‘‘structured finance,’’ 60, 64–68, 72, 133, 175–176, 185 sub prime, 55, 63–64, 176, 185 SVA (Shareholder Value Added), 71 Sweden banking crisis, 166 TARP (Troubled Asset Relief Program), 170 technology in banking and finance, xviii, 11, 40, 61–62, 70, 100, 117, 184 Term Loans, defined, 38–39; history, 143, 146 Thatcher, Margaret, 182, 184, 188 Thrift. See S&L ‘‘Too Big To Fail’’ doctrine, 159, 174 ‘‘Toxic Assets,’’ 72 Uniform Commercial Code, 38 U.S. Treasury, 44, 156, 158, 163, 173 VAR (Value at Risk), explained, 68; uses and abuses of, 69, 71 venture capital, 26–27 ‘‘volatility’’ (of financial markets, of stock and bond prices), 48–49 Volcker, Paul and end of the Great Inflation, 130, 140 Von Clemm, Michael, and Eurodollar CD, 149 Wall Street (short hand for financial economy), 1, 18–19, 22, 24, 57, 91, 102, 104–106, 138–140, 156–157, 159, 176–177, 183, 185 Warburg, Sigmund, and Eurodollar markets, 151 ‘‘working capital’’ and bank lending, 61, 143 World Bank, 115 Wriston, Walt, 149, 151–52; and the invention of the Certificate of Deposit, 145 Zombinakis, Minos, and Eurodollar markets, 148, 151 About the Author KEVIN MELLYN has over 30 years of experience in banking and consulting in London and New York with special emphasis on wholesale financial markets and their supporting technologies and infrastructure.


pages: 348 words: 83,490

More Than You Know: Finding Financial Wisdom in Unconventional Places (Updated and Expanded) by Michael J. Mauboussin

Alan Greenspan, Albert Einstein, Andrei Shleifer, Atul Gawande, availability heuristic, beat the dealer, behavioural economics, Benoit Mandelbrot, Black Swan, Brownian motion, butter production in bangladesh, buy and hold, capital asset pricing model, Clayton Christensen, clockwork universe, complexity theory, corporate governance, creative destruction, Daniel Kahneman / Amos Tversky, deliberate practice, demographic transition, discounted cash flows, disruptive innovation, diversification, diversified portfolio, dogs of the Dow, Drosophila, Edward Thorp, en.wikipedia.org, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, fixed income, framing effect, functional fixedness, hindsight bias, hiring and firing, Howard Rheingold, index fund, information asymmetry, intangible asset, invisible hand, Isaac Newton, Jeff Bezos, John Bogle, Kenneth Arrow, Laplace demon, Long Term Capital Management, loss aversion, mandelbrot fractal, margin call, market bubble, Menlo Park, mental accounting, Milgram experiment, Murray Gell-Mann, Nash equilibrium, new economy, Paul Samuelson, Performance of Mutual Funds in the Period, Pierre-Simon Laplace, power law, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, Richard Florida, Richard Thaler, Robert Shiller, shareholder value, statistical model, Steven Pinker, stocks for the long run, Stuart Kauffman, survivorship bias, systems thinking, The Wisdom of Crowds, transaction costs, traveling salesman, value at risk, wealth creators, women in the workforce, zero-sum game

The bad news, as physicist Phil Anderson notes above, is that the tails of the distribution often control the world. Tell Tail Normal distributions are the bedrock of finance, including the random walk, capital asset pricing, value-at-risk (VaR), and Black-Scholes models. Value-at-risk models, for example, attempt to quantify how much loss a portfolio may suffer with a given probability. While there are various forms of VaR models, a basic version relies on standard deviation as a measure of risk. Given a normal distribution, it is relatively straightforward to measure standard deviation, and hence risk. However, if price changes are not normally distributed, standard deviation can be a very misleading proxy for risk.2 The research, some done as far back as the early 1960s, shows that price changes do not follow a normal distribution.

theory: attribute-based approach; building, steps of; falsifiability Thorp, Ed time horizons timing rules tipping point total return to shareholders (TRS) tracking error transaction costs traveling-salesman problem Treynor, Jack Tupperware parties Tversky, Amos Twain, Mark two-by-two matrix Ulysses uncertainty classifications expectations and U.S. Steel utility Utterback, James valuation, investor evolution and value-at-risk (VaR) models value investors value traps, downturns volatility “Vox Populi” (Galton) Waldrop, Mitchell Watts, Duncan weak signals wealth, isolated components vs. total Welch, Ivo Welch, Jack Wermers, Russ wheel of fortune experiment Why Stock Markets Crash (Sornette) Wiggins, Robert R.

Jackwerth and Rubinstein note that assuming annualized volatility of 20 percent for the market and a lognormal distribution, the 29 percent drop in the S&P 500 futures was a twenty-seven-standard-deviation event, with a probability of 10−160. 5 Per Bak, How Nature Works (New York: Springer-Verlag, 1996). 6 See chapter 22. 7 Sushil Bikhchandani and Sunil Sharma, “Herd Behavior in Financial Markets,” IMF Staff Papers 47, no. 3 (September 2001); see http://www.imf. org/External/Pubs/FT/staffp/2001/01/pdf/Bikhchan.pdf. 8 Michael S. Gibson, “Incorporating Event Risk into Value-at-Risk,” The Federal Reserve Board Finance and Economics Discussion Series, 2001-17 (February 2001); see http://www.federalreserve.gov/pubs/feds/2001/200117/200117abs.html. 32. Integrating the Outliers 1 Daniel Bernoulli, “Exposition of a New Theory on the Measurement of Risk,” Econometrica, 22 (January 1954): 23-36.


Humble Pi: A Comedy of Maths Errors by Matt Parker

8-hour work day, Affordable Care Act / Obamacare, bitcoin, British Empire, Brownian motion, Chuck Templeton: OpenTable:, collateralized debt obligation, computer age, correlation does not imply causation, crowdsourcing, Donald Trump, fake news, Flash crash, forensic accounting, game design, High speed trading, Julian Assange, millennium bug, Minecraft, Neil Armstrong, null island, obamacare, off-by-one error, orbital mechanics / astrodynamics, publication bias, Richard Feynman, Richard Feynman: Challenger O-ring, selection bias, SQL injection, subprime mortgage crisis, Tacoma Narrows Bridge, Therac-25, value at risk, WikiLeaks, Y2K

But the chain of mistakes featured some serious spreadsheet abuse, including the calculation of how big the risk was and how losses were being tracked. A Value at Risk (aka VaR) calculation gives traders a sense of how big the current risk is and limits what sorts of trades are allowed within the company’s risk policies. But when that risk is underestimated and the market takes a turn for the worse, a lot of money can be lost. Amazingly, one specific Value at Risk calculation was being done in a series of Excel spreadsheets with values having to be manually copied between them. I get the feeling it was a prototype model for working out the risk that was put into production without being converted over to a real system for doing mathematical modelling calculations.

I get the feeling it was a prototype model for working out the risk that was put into production without being converted over to a real system for doing mathematical modelling calculations. And enough errors accumulated in the spreadsheets to underestimate the VaR. An overestimation of risk would have meant that more money was kept safe than should have been, and because it was limiting trades it would have caused someone to investigate what was going on. An underestimation of VaR silently let people keep risking more and more money. But surely these losses would be noticed by someone. The traders regularly gave their portfolio positions ‘marks’ to indicate how well or badly they were doing.

That very code was used in 2008 to attack the UK government and the United Nations – except some of it had been converted into hexadecimal values to slip by security systems looking for incoming code. Once in the database, it would unzip back into computer code, find the database entries then phone home to download additional malicious programs. This is it when it was camouflaged: script.asp?var=random';DECLARE%20@S%20NVARCHAR(4000);SET%20@S=CAST(0x4400450043004C004100520045002000400054002000760061007200630068006100720028 … [another 1,920 digits] 004F00430041005400450020005400610062006C0065005F0043007500720073006F007200%20AS%20NVARCHAR(4000));EXEC(@S);-- Sneaky, huh? From unfortunate names to malicious attacks, running a database is difficult.


pages: 376 words: 109,092

Paper Promises by Philip Coggan

accounting loophole / creative accounting, activist fund / activist shareholder / activist investor, Alan Greenspan, balance sheet recession, bank run, banking crisis, barriers to entry, Bear Stearns, Berlin Wall, Bernie Madoff, Black Monday: stock market crash in 1987, Black Swan, bond market vigilante , Bretton Woods, British Empire, business cycle, call centre, capital controls, Carmen Reinhart, carried interest, Celtic Tiger, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, currency risk, debt deflation, delayed gratification, diversified portfolio, eurozone crisis, Fall of the Berlin Wall, falling living standards, fear of failure, financial innovation, financial repression, fixed income, floating exchange rates, full employment, German hyperinflation, global reserve currency, Goodhart's law, Greenspan put, hiring and firing, Hyman Minsky, income inequality, inflation targeting, Isaac Newton, John Meriwether, joint-stock company, junk bonds, Kenneth Rogoff, Kickstarter, labour market flexibility, Les Trente Glorieuses, light touch regulation, Long Term Capital Management, low interest rates, manufacturing employment, market bubble, market clearing, Martin Wolf, Minsky moment, Money creation, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, Myron Scholes, negative equity, Nick Leeson, Northern Rock, oil shale / tar sands, paradox of thrift, peak oil, pension reform, plutocrats, Ponzi scheme, price stability, principal–agent problem, purchasing power parity, quantitative easing, QWERTY keyboard, railway mania, regulatory arbitrage, reserve currency, Robert Gordon, Robert Shiller, Ronald Reagan, savings glut, short selling, South Sea Bubble, sovereign wealth fund, special drawing rights, Suez crisis 1956, The Chicago School, The Great Moderation, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Wealth of Nations by Adam Smith, time value of money, too big to fail, trade route, tulip mania, value at risk, Washington Consensus, women in the workforce, zero-sum game

But perhaps the banks deceived themselves in the long run. One particular risk measure, called value-at-risk (VAR), got built into the system in the wake of Black Monday. Dennis Weatherstone, the chief executive of J P Morgan, was disturbed by the events of 1987. He asked his team to provide a measure of how much the bank was exposed to sudden market movements. VAR was developed to provide that information; it aimed to measure the maximum loss the bank could suffer on 95 per cent (in some cases 99 per cent) of all trading days. Some see the use of VAR as contributing to the crisis by providing false comfort to bank executives.

Rubin, Robert Rueff, Jacques Rumsfeld, Donald Russia Sack, Alexander St Augustine Saint-Simon, duc de Salamis (city) Santelli, Rick Sarkozy, Nicholas Saudi Arabia savings savings glut Sbrancia, Belen Schacht, Hjalmar Scholes, Myron shale gas Second Bank of the United States Second World War Securities and Exchange Commission seignorage Shakespeare, William share options Shiller, Robert short-selling silver Singapore Sloan, Alfred Smith, Adam Smith, Fred Smithers & Co Smithsonian agreement Snowden, Philip Socialist Party of Greece social security Société Générale solidus Solon of Athens Soros, George sound money South Africa South Korea South Sea bubble sovereign debt crisis Soviet Union Spain special drawing right speculation, speculators Stability and Growth pact stagnation Standard & Poor’s sterling Stewart, Jimmy Stiglitz, Joseph stock markets stop-go cycle store of value Strauss-Kahn, Dominque Strong, Benjamin sub-prime lending Suez canal crisis Suharto, President of Indonesia Sumerians supply-side reforms Supreme Court (US) Sutton, Willie Sweden Swiss franc Swiss National Bank Switzerland Sylla, Richard Taiwan Taleb, Nassim Nicholas taxpayers Taylor, John tea party (US) Temin, Peter Thackeray, William Makepeace Thailand Thatcher, Margaret third world debt crisis Tiernan, Tommy Times Square, New York tobacco as currency treasury bills treasury bonds Treaty of Versailles trente glorieuses Triana, Pablo Triffin, Robert Triffin dilemma ‘trilemma’ of currency policy Truck Act True Finn party Truman, Harry S tulip mania Turkey Turner, Adair Twain, Mark unit of account usury value-at-risk (VAR) Vanguard Vanity Fair Venice Vietnam War vigilantes, bond market Viniar, David Volcker, Paul Voltaire Wagner, Adolph Wall Street Wall Street Crash of 1929 Wal-Mart wampum Warburton, Peter Warren, George Washington consensus Weatherstone, Dennis Weimar inflation Weimar Republic Weinberg, Sidney West Germany whales’ teeth White, Harry Dexter William of Orange Wilson, Harold Wirtschaftswunder Wizard of Oz, The Wolf, Martin Women Empowering Women Woodward, Bob Woolley, Paul World Bank Wriston, Walter Xinhua agency Yale University yen yield on debt yield on shares Zambia zero interest rates Zimbabwe Zoellick, Robert Philip Coggan is the Buttonwood columnist of the Economist.

In markets, we get ‘fat tails’ of the bell curve, or more extreme examples than one might expect. David Viniar, chief financial officer of Goldman Sachs, said in August 2007, ‘We were seeing things that were 25-standard deviation moves, several days in a row.’25 Since, under a bell curve, 25-standard deviations have an infinitesimal chance of occurring, this shows that the VAR model was simply wrong. Of course, modellers can allow for different probability distributions than the bell curve. But they still don’t know which distribution will occur. Take too cautious a view and you will take little risk, and some other investment bank will take all the profits. To the aggressive heads of investment banks like Dick Fuld and Jimmy Cayne, this was the clinching argument.


pages: 741 words: 179,454

Extreme Money: Masters of the Universe and the Cult of Risk by Satyajit Das

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", "there is no alternative" (TINA), "World Economic Forum" Davos, affirmative action, Alan Greenspan, Albert Einstein, algorithmic trading, Andy Kessler, AOL-Time Warner, Asian financial crisis, asset allocation, asset-backed security, bank run, banking crisis, banks create money, Basel III, Bear Stearns, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, Bonfire of the Vanities, bonus culture, book value, Bretton Woods, BRICs, British Empire, business cycle, buy the rumour, sell the news, capital asset pricing model, carbon credits, Carl Icahn, Carmen Reinhart, carried interest, Celtic Tiger, clean water, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, corporate raider, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, Daniel Kahneman / Amos Tversky, deal flow, debt deflation, Deng Xiaoping, deskilling, discrete time, diversification, diversified portfolio, Doomsday Clock, Dr. Strangelove, Dutch auction, Edward Thorp, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, eurozone crisis, Everybody Ought to Be Rich, Fall of the Berlin Wall, financial engineering, financial independence, financial innovation, financial thriller, fixed income, foreign exchange controls, full employment, Glass-Steagall Act, global reserve currency, Goldman Sachs: Vampire Squid, Goodhart's law, Gordon Gekko, greed is good, Greenspan put, happiness index / gross national happiness, haute cuisine, Herman Kahn, high net worth, Hyman Minsky, index fund, information asymmetry, interest rate swap, invention of the wheel, invisible hand, Isaac Newton, James Carville said: "I would like to be reincarnated as the bond market. You can intimidate everybody.", job automation, Johann Wolfgang von Goethe, John Bogle, John Meriwether, joint-stock company, Jones Act, Joseph Schumpeter, junk bonds, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, laissez-faire capitalism, load shedding, locking in a profit, Long Term Capital Management, Louis Bachelier, low interest rates, margin call, market bubble, market fundamentalism, Market Wizards by Jack D. Schwager, Marshall McLuhan, Martin Wolf, mega-rich, merger arbitrage, Michael Milken, Mikhail Gorbachev, Milgram experiment, military-industrial complex, Minsky moment, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, mortgage tax deduction, mutually assured destruction, Myron Scholes, Naomi Klein, National Debt Clock, negative equity, NetJets, Network effects, new economy, Nick Leeson, Nixon shock, Northern Rock, nuclear winter, oil shock, Own Your Own Home, Paul Samuelson, pets.com, Philip Mirowski, Phillips curve, planned obsolescence, plutocrats, Ponzi scheme, price anchoring, price stability, profit maximization, proprietary trading, public intellectual, quantitative easing, quantitative trading / quantitative finance, Ralph Nader, RAND corporation, random walk, Ray Kurzweil, regulatory arbitrage, Reminiscences of a Stock Operator, rent control, rent-seeking, reserve currency, Richard Feynman, Richard Thaler, Right to Buy, risk free rate, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, Rod Stewart played at Stephen Schwarzman birthday party, rolodex, Ronald Reagan, Ronald Reagan: Tear down this wall, Satyajit Das, savings glut, shareholder value, Sharpe ratio, short selling, short squeeze, Silicon Valley, six sigma, Slavoj Žižek, South Sea Bubble, special economic zone, statistical model, Stephen Hawking, Steve Jobs, stock buybacks, survivorship bias, tail risk, Teledyne, The Chicago School, The Great Moderation, the market place, the medium is the message, The Myth of the Rational Market, The Nature of the Firm, the new new thing, The Predators' Ball, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, trickle-down economics, Turing test, two and twenty, Upton Sinclair, value at risk, Yogi Berra, zero-coupon bond, zero-sum game

Corporations and investors increasingly needed to use available derivatives to hedge risks to be considered prudent. The BSM model and Markowitz’s work evolved into risk quantification modes, such as value at risk (VAR) models. VAR signifies the maximum amount that you can lose, statistically, as a result of market moves for a given probability over a fixed time. If you own shares over a year, then most of the time the share price moves up or down a small amount. On some days you may get a large or very large price change. VAR ranks the price changes from largest fall to largest rise. Assuming that prices follow a random walk and price changes fit a normal distribution, you can calculate the probability of a particular size price change.

MacKenzie, An Engine, Not a Camera: 79. 17. Ibid: 79, 80. 18. Ibid: 80. 19. Ibid: 83, 84. 20. Joel Stern “Let’s abandon earnings per share” (18 December 1972) Wall Street Journal. 21. MacKenzie, An Engine, Not a Camera: 254. 22. Barry Schachter “An irreverent guide to value at risk” (August 1997) Financial Engineering News 1/1 (www.debtonnet.com/newdon/files/marketinformation/var-guide.asp). 23. Quoted in Fox, The Myth of the Rational Market: 191. 24. Quoted in ibid: 260. 25. Paul De Grauwe, Leonardo Iania and Pablo Rovira Kaltwasser “How abnormal was the stock market in October 2008?” (11 November 2008) (www.eurointelligence.com/article.581+M5f21b8d26a3.0.html). 26.

Treasury bonds, 87 UBS, 201 UK Financial Services Act (2001), 279 UK House of Commons, 288 ultra prosperity, 99 uncertainty, 366 unintended consequences, 130 United Airlines (UAL), 166 United States debt levels as a percentage of GDP, 265 domestic corporate profits, 276 universal banks, 75 University of California at Berkeley, 42 University of Chicago, 34, 101, 104 collaboration between Black and Scholes, 121 University of Surrey, 363 Unocal, 137 Updike, John, 27, 46, 363 Urbanek, Zdener, 91 urbanization, 38 Urdu, 22 ushinawareta junen (the lost decade), 39 usuries, 32 V vacancy rates, commercial real estate, 349 VADM (very accurately defined maturity) bonds, 178 value accounting, 286-287 of commodities, 24 of modern money, 35 stocks, 58 value at risk (VAR) models, 125 Van der Starr, Cornelius, 230 Van Riper, Paul, 264 Vanguard Group, 123 Vanity Fair, 324 vapidity of life, 328 VAT (value added tax), 262 Veblen, Thorstein, 41, 52, 245 Veil, Jean, 229 velocity of capital, 69 Velvet Revolution (1989), 359 vertical segmentation, 170. See also tranches Vertin, James, 123 Viagra, 326 video, financial news, 91-99 Vienna landmark, 163 Vietnam War, 30, 274 Viniar, David, 126, 198 Virgil, 338 virtual loans, 195-196 volatility, 254 hedge funds, 246 LCTM, 250 of currencies, 125 Volcker rule, 353 Volcker, Paul, 78, 145, 352, 359 Volkswagen (VW), 55, 146, 257 Volvo AB, 343 von Bismarck.


pages: 265 words: 93,231

The Big Short: Inside the Doomsday Machine by Michael Lewis

Alan Greenspan, An Inconvenient Truth, Asperger Syndrome, asset-backed security, Bear Stearns, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, diversified portfolio, facts on the ground, financial engineering, financial innovation, fixed income, forensic accounting, Gordon Gekko, high net worth, housing crisis, illegal immigration, income inequality, index fund, interest rate swap, John Meriwether, junk bonds, London Interbank Offered Rate, Long Term Capital Management, low interest rates, medical residency, Michael Milken, money market fund, moral hazard, mortgage debt, pets.com, Ponzi scheme, Potemkin village, proprietary trading, quantitative trading / quantitative finance, Quicken Loans, risk free rate, Robert Bork, short selling, Silicon Valley, tail risk, the new new thing, too big to fail, value at risk, Vanguard fund, zero-sum game

The $16 billion in subprime risk Hubler had taken on showed up in Morgan Stanley's risk reports inside a bucket marked "triple A"--which is to say, they might as well have been U.S. Treasury bonds. They showed up again in a calculation known as value at risk (VaR). The tool most commonly used by Wall Street management to figure out what their traders had just done, VaR measured only the degree to which a given stock or bond had jumped around in the past, with the recent movements receiving a greater emphasis than movements in the more distant past. Having never fluctuated much in value, triple-A-rated subprime-backed CDOs registered on Morgan Stanley's internal reports as virtually riskless.

Those are big fat tail risks that caught us hard, right. That's what happened. TANONA: Okay. Fair enough. I guess the other thing I would question. I am surprised that your trading VaR stayed stable in the quarter given this level of loss, and given that I would suspect that these were trading assets. So can you help me understand why your VaR didn't increase in the quarter dramatically?+ MACK: Bill, I think VaR is a very good representation of liquid trading risk. But in terms of the (inaudible) of that, I am very happy to get back to you on that when we have been out of this, because I can't answer that at the moment.


pages: 537 words: 144,318

The Invisible Hands: Top Hedge Fund Traders on Bubbles, Crashes, and Real Money by Steven Drobny

Albert Einstein, AOL-Time Warner, Asian financial crisis, asset allocation, asset-backed security, backtesting, banking crisis, Bear Stearns, Bernie Madoff, Black Swan, bond market vigilante , book value, Bretton Woods, BRICs, British Empire, business cycle, business process, buy and hold, capital asset pricing model, capital controls, central bank independence, collateralized debt obligation, commoditize, commodity super cycle, commodity trading advisor, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, debt deflation, diversification, diversified portfolio, equity premium, equity risk premium, family office, fiat currency, fixed income, follow your passion, full employment, George Santayana, global macro, Greenspan put, Hyman Minsky, implied volatility, index fund, inflation targeting, interest rate swap, inventory management, inverted yield curve, invisible hand, junk bonds, Kickstarter, London Interbank Offered Rate, Long Term Capital Management, low interest rates, market bubble, market fundamentalism, market microstructure, Minsky moment, moral hazard, Myron Scholes, North Sea oil, open economy, peak oil, pension reform, Ponzi scheme, prediction markets, price discovery process, price stability, private sector deleveraging, profit motive, proprietary trading, purchasing power parity, quantitative easing, random walk, Reminiscences of a Stock Operator, reserve currency, risk free rate, risk tolerance, risk-adjusted returns, risk/return, savings glut, selection bias, Sharpe ratio, short selling, SoftBank, sovereign wealth fund, special drawing rights, statistical arbitrage, stochastic volatility, stocks for the long run, stocks for the long term, survivorship bias, tail risk, The Great Moderation, Thomas Bayes, time value of money, too big to fail, Tragedy of the Commons, transaction costs, two and twenty, unbiased observer, value at risk, Vanguard fund, yield curve, zero-sum game

Some people can trade markets using only numbers, prices on a screen, but this approach does not work for me. The numbers have to mean something—I have to understand the fundamental drivers behind the numbers. And while fundamentals are important, they are only one of many important inputs to the process. Just as a Value-at-Risk (VaR) model alone cannot tell you what your overall risk is, economic analysis alone cannot tell you where the bond market should be. Let us use an interest rate trade around central bank policy as a straightforward example to illustrate my process. Economic drivers will create the framework: What is the outlook for growth, inflation, employment, and other key variables?

At least that is how it has worked for me. Having said that, the commodities markets have been conducive to this approach in recent years. Still, I believe the key is running a fairly diverse portfolio of good risk-versus-reward trades coupled with very careful risk management on a portfolio level. It is important to have limits on VaR (value at risk), on margin-to-equity, portfolio P&L volatility, sector risk, individual position risk, vega, theta and premium spent if you trade a lot of options. It is also extremely important to delever the portfolio when you’re losing money in order to preserve capital. What’s the difference between being a prop trader and being a hedge fund manager?

More overlooked were investments in assets that were liquid in good times but became very illiquid in periods of stress, including external managers who threw up gates, credit derivatives whereby whole tranches became toxic, and even crowded trades such as single stocks chosen according to well-known quantitative screens. As a result, when the liquidity crisis hit, it hit everywhere all at once, creating devastating effects. Finally, 2008 also exposed faults in the traditional methods of calculating risk. No matter how your plan went about calculating value at risk (VaR)—historical, parametric, Monte Carlo, or other—the statisticians claimed that the more data used to calculate the parameters (i.e., the further the look-back in time), the better. But 2008 showed that this is not necessarily correct, especially for the liquid portions of our investments. The result was that almost all real money funds got caught badly off-guard with the ferociousness and comprehensiveness of the market declines.


pages: 239 words: 74,845

The Antisocial Network: The GameStop Short Squeeze and the Ragtag Group of Amateur Traders That Brought Wall Street to Its Knees by Ben Mezrich

4chan, Asperger Syndrome, Bayesian statistics, bitcoin, Carl Icahn, contact tracing, data science, democratizing finance, Dogecoin, Donald Trump, Elon Musk, fake news, gamification, global pandemic, Google Hangouts, Hyperloop, meme stock, Menlo Park, payment for order flow, Pershing Square Capital Management, Robinhood: mobile stock trading app, security theater, short selling, short squeeze, Silicon Valley, Silicon Valley startup, social distancing, Tesla Model S, too big to fail, Two Sigma, value at risk, wealth creators

While the customer’s money was temporarily put aside, essentially in an untouchable safe, for the two days it took for the clearing agency to verify that both parties were able to provide what they had agreed upon—the brokerage house, Robinhood—had to insure the deal with a deposit; money of its own, separate from the money that the customer had provided, that could be used to guarantee the value of the trade. In financial parlance, this “collateral” was known as VAR—or value at risk. For a single trade of a simple asset, it would have been relatively easy to know how much the brokerage would need to deposit to insure the situation; the risk of something going wrong would be small, and the total value would be simple to calculate. If GME was trading at $400 a share and a customer wanted ten shares, there was $4000 at risk, plus or minus some nominal amount due to minute vagaries in market fluctuations during the two-day period before settlement.

Once he’d gotten his liaison at the agency on the phone, he’d broken down the charge into two parts. The NSCC’s algorithm had taken in all the risk inputs from the trading volumes and volatility of the day before and come up with a VAR of $1.3 billion, and had added to that an “excess capital premium charge” of an additional $2.2 billion and change. This additional charge had been added because the original VAR charge far exceeded Robinhood’s net capital—thus the original call being so large that Robinhood didn’t have the cash on hand to cover it had led to a multiplying effect—an additional charge to cover the risk of Robinhood not being able to cover.

There was no way to offset some of the risk with sell trades; and since GameStop was itself an inherently risky equity, with such a huge short float, the overall risk grew exponentially. But still, Vlad had never seen or imagined anything like this. To compare, the biggest special charge Robinhood had ever gotten from the NSCC before had been for $25 million. Now the NSCC was asking for an additional $2.2 billion, on top of its $1.3 billion VAR. Once Jim had confirmed that the numbers weren’t a mistake, that the charges were real and they only had until 10:00 a.m. to make good on that ungodly deposit, the question quickly became—what could they do? First and foremost, despite what anybody might think or say or publish after the fact or scream on Twitter or Reddit or Clubhouse, Vlad believed that his main responsibility was to the Robinhood users.


pages: 1,544 words: 391,691

Corporate Finance: Theory and Practice by Pierre Vernimmen, Pascal Quiry, Maurizio Dallocchio, Yann le Fur, Antonio Salvi

"Friedman doctrine" OR "shareholder theory", accelerated depreciation, accounting loophole / creative accounting, active measures, activist fund / activist shareholder / activist investor, AOL-Time Warner, ASML, asset light, bank run, barriers to entry, Basel III, Bear Stearns, Benoit Mandelbrot, bitcoin, Black Swan, Black-Scholes formula, blockchain, book value, business climate, business cycle, buy and hold, buy low sell high, capital asset pricing model, carried interest, collective bargaining, conceptual framework, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, currency risk, delta neutral, dematerialisation, discounted cash flows, discrete time, disintermediation, diversification, diversified portfolio, Dutch auction, electricity market, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, eurozone crisis, financial engineering, financial innovation, fixed income, Flash crash, foreign exchange controls, German hyperinflation, Glass-Steagall Act, high net worth, impact investing, implied volatility, information asymmetry, intangible asset, interest rate swap, Internet of things, inventory management, invisible hand, joint-stock company, joint-stock limited liability company, junk bonds, Kickstarter, lateral thinking, London Interbank Offered Rate, low interest rates, mandelbrot fractal, margin call, means of production, money market fund, moral hazard, Myron Scholes, new economy, New Journalism, Northern Rock, performance metric, Potemkin village, quantitative trading / quantitative finance, random walk, Right to Buy, risk free rate, risk/return, shareholder value, short selling, Social Responsibility of Business Is to Increase Its Profits, sovereign wealth fund, Steve Jobs, stocks for the long run, supply-chain management, survivorship bias, The Myth of the Rational Market, time value of money, too big to fail, transaction costs, value at risk, vertical integration, volatility arbitrage, volatility smile, yield curve, zero-coupon bond, zero-sum game

A company can have a strategically important position in oil, coffee, semiconductors or electricity markets, for example. 3. Value at risk (VaR) and corporate value at risk VaR is a finer measure of market risk. It represents an investor’s maximum potential loss on the value of an asset or a portfolio of financial assets and liabilities, based on the investment timeframe and a confidence interval. This potential loss is calculated on the basis of historical data or deduced from normal statistical laws. Hence, a portfolio worth €100m with a VaR of €2.5m at 95% (calculated on a monthly basis) has just a 5% chance of shrinking more than €2.5m in one month. VaR is often used by financial establishments as a tool in managing risk.

Rennie, The Oxford Handbook of Credit Derivatives, Oxford University Press, 2011. http://www.credit-deriv.com On the transfer of alternative risks: K. Froot, The market for catastrophe risk: A clinical examination, Journal of Financial Economics, 60(2&3), 529–571, May 2001. On value at risk: M. Choudhry, An Introduction to Value-at-Risk, John Wiley & Sons, Inc., 2013. P. Jorion, Value at Risk, 3rd edn, McGraw-Hill, 2006. M. Leippold, Don’t rely on VaR, Euromoney, 36–49, November 2004. On political risk: M. Bouchet, E. Clark, B. Groslambert, Country Risk Assessment: A Guide to Global Investment Strategy, John Wiley & Sons, Ltd, 2003. On pension obligations: G.

unitranch debt unlevered beta (beta assets) unlimited companies unlisted companies unsolicited offers unwinding of contracts US income statement formats US listings, non-US companies US-style options “used” financial products validation of deliveries valuation errors effects valuation/valuation methods annuity/perpetuity comparison of consolidated accounts cost of capital equity inventories overview P/E principle reconciling differences shareholders’ equity volatility young companies value capital employed corporate governance finance creating future contracts illustration implications organisation theories and tax shield taxation and theoretical foundation values distinction value added value additivity rule value of assets value chain value of company, distributing value creation criteria internal financing investment choice limitations M&A deals markets in equilibrium measuring NPV equal to postponed projects real estate uncertainty working capital value dating value of debt see also value of net debt value of equity capital structure enterprise value equation of methods multiples based on options theory reinvested cash flow value of equity capital value of flexibility value of hybrids convertible bonds mandatory convertibles preference shares warrants value of investments value investors value of net debt value of option binomial method intrinsic value at maturity parameters real options risk time value volatility value at risk (VaR) value of security capital markets cost of capital falling/rising financial manager’s role financial markets maximising risk/fluctuation yield curves value stocks value transfer, bankruptcy value in use values–value distinction VaR see value at risk variable costs, breakeven point variable-income securities variance analysis risk-analysis volatility and VDD see vendor due diligence vega vendor due diligence (VDD) venture capital funds venture capitalists shareholders’ agreement valuing young companies vertical integration vesting period, stock options veto power, blocking minority viability of company Vishny, R.


pages: 467 words: 154,960

Trend Following: How Great Traders Make Millions in Up or Down Markets by Michael W. Covel

Albert Einstein, Alvin Toffler, Atul Gawande, backtesting, Bear Stearns, beat the dealer, Bernie Madoff, Black Swan, buy and hold, buy low sell high, California energy crisis, capital asset pricing model, Carl Icahn, Clayton Christensen, commodity trading advisor, computerized trading, correlation coefficient, Daniel Kahneman / Amos Tversky, delayed gratification, deliberate practice, diversification, diversified portfolio, Edward Thorp, Elliott wave, Emanuel Derman, Eugene Fama: efficient market hypothesis, Everything should be made as simple as possible, fiat currency, fixed income, Future Shock, game design, global macro, hindsight bias, housing crisis, index fund, Isaac Newton, Jim Simons, John Bogle, John Meriwether, John Nash: game theory, linear programming, Long Term Capital Management, managed futures, mandelbrot fractal, margin call, market bubble, market fundamentalism, market microstructure, Market Wizards by Jack D. Schwager, mental accounting, money market fund, Myron Scholes, Nash equilibrium, new economy, Nick Leeson, Ponzi scheme, prediction markets, random walk, Reminiscences of a Stock Operator, Renaissance Technologies, Richard Feynman, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, shareholder value, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, survivorship bias, systematic trading, Teledyne, the scientific method, Thomas L Friedman, too big to fail, transaction costs, upwardly mobile, value at risk, Vanguard fund, William of Occam, zero-sum game

We are influenced heavily by standard finance theory that revolves almost entirely around normal distribution worship. Michael Mauboussin and Kristen Bartholdson see clearly the state of affairs: “Normal distributions are the bedrock of finance, including the random walk, capital asset pricing, value-at-risk, and Black-Scholes models. Value-at-risk (VaR) models, for example, attempt to quantify how much loss a portfolio may suffer with a given probability. While there are various forms of VaR models, a basic version relies on standard deviation as a measure of risk. Given a normal distribution, it is relatively straightforward to measure standard deviation, and hence risk. However, if price changes are not normally distributed, standard deviation can be a very misleading proxy for risk.”14 Chapter 8 • Science of Trading The problem with using standard deviation as a risk measurement can be seen with the example where two traders have similar standard deviations, but might show entirely different distribution of returns.

See stocks, trend following and success of, 15-17 understand and explaining to clients, 280-281 Tropin, Ken, 271, 274, 289 trusting numbers, 18 truth, refusal of, and behavioral finance, 196 TTP (Trading Tribe Process), 203 Turtles, 78-79, 281 correlation, 113-114 selection process, 79-83 Tversky, Amos, 189 U.S. dollar trading, 128, 136, 139, 162 U.S. National Agricultural Library, 53 U.S. T-Bond chart (1998), trend-followers and, 159 UBS, 156 Ueland, Brenda, 24 uncertainty, reaction to, 197 understanding trend following, 280-281 upside volatility, 102-105 value-at-risk (VAR) models, 180 van Stolk, Mark, 262 Vandergrift, Justin, 110, 132-134, 255 Vanguard, 295 VAR (value-at-risk) models, 180 Varanedoe, J. Kirk T., 214 Vician, Thomas, Jr., 40, 66, 243, 272 volatility, 99-105 measuring, 180 risk versus, 104 upside volatility, 102-105 Voltaire, xvii von Metternich, Klemens, 270 von Mises, Ludwig, xviii, 3, 97, 99, 202, 264 Wachtel, Larry, 235 Waksman, Sol, 253 Watts, Dickson, 92 Weaver, Earl, 182 web sites, 397 Weill, Sandy, 156 Weintraub, Neal T., 233 Wells, Herbert George (H.G.), 225 Welton, Patrick, 15 what to trade, 254-256 when to buy/sell, 259-262 whipsaws, 263 Wigdor, Paul, 126 Wilcox, Cole, 268, 307 William of Occam, 213 Williams, Ted, 261 winners Long-Term Capital Management (LTCM) collapse, 156-164 losers versus, 123-125 “The Winners and Losers of the Zero-Sum Game: The Origins of Trading Profits, Price Efficiency and Market Liquidity” (white paper) (Harris), 115 winning investment philosophies, 4-6 winning positions, when to exit, 263-265 Winton Capital Management, 29, 372-373 Winton Futures Fund, 230 “The Winton Papers” (Harding), 31 Wittgenstein, Ludwig, 395 Womack, Kent, 241 The World is Flat (Friedman), 143 WorldCom, 241 Wright, Charlie, 244 Yahoo!

They draw conclusions about one month’s performance and forget to look at the long term. Just like a batting average, which can have short-term streaks over the course of a season, trend followers have streaks. Trend following performance does deviate from averages, but over time there is remarkable consistency. • Value-at-risk (VAR) models measure volatility, not risk. If you rely on VAR as a risk measure you are in trouble. • Hunt Taylor, Director of Investments, Stern Investment Holdings, states: “I’m wondering when statisticians are going to figure out that the statistical probability of improbable losses are absolutely the worst predictors of the regularity with which they’ll occur.


pages: 892 words: 91,000

Valuation: Measuring and Managing the Value of Companies by Tim Koller, McKinsey, Company Inc., Marc Goedhart, David Wessels, Barbara Schwimmer, Franziska Manoury

accelerated depreciation, activist fund / activist shareholder / activist investor, air freight, ASML, barriers to entry, Basel III, Black Monday: stock market crash in 1987, book value, BRICs, business climate, business cycle, business process, capital asset pricing model, capital controls, Chuck Templeton: OpenTable:, cloud computing, commoditize, compound rate of return, conceptual framework, corporate governance, corporate social responsibility, creative destruction, credit crunch, Credit Default Swap, currency risk, discounted cash flows, distributed generation, diversified portfolio, Dutch auction, energy security, equity premium, equity risk premium, financial engineering, fixed income, index fund, intangible asset, iterative process, Long Term Capital Management, low interest rates, market bubble, market friction, Myron Scholes, negative equity, new economy, p-value, performance metric, Ponzi scheme, price anchoring, proprietary trading, purchasing power parity, quantitative easing, risk free rate, risk/return, Robert Shiller, Savings and loan crisis, shareholder value, six sigma, sovereign wealth fund, speech recognition, stocks for the long run, survivorship bias, technology bubble, time value of money, too big to fail, transaction costs, transfer pricing, two and twenty, value at risk, yield curve, zero-coupon bond

Estimate the risk weighting and RWA for each of the loan categories in such a way that your estimate fits the reported RWA for all loans (€202 billion in this example). r Market risk is a bank’s exposure to changes in interest rates, stock prices, currency rates, and commodity prices. It is typically related to its value at risk (VaR), which is the maximum loss for the bank under a worst-case scenario of a given probability for these market prices. For an approximation, use the reported VaR over several years to estimate the bank’s RWA as a percentage of VaR (242 percent in the example). r Operational risk is all risk that is neither market nor credit risk. It is usu- ally related to a bank’s net revenues (net interest income plus net other income).

You can think of a bank’s trading results as driven by the size of its trading positions, the risk taken in trading (as measured by the total VaR), and the trading result per unit of risk (measured by return on VaR). The ratio of VaR to net trading position is an indication of the relative risk taking in trading. The more risk a bank takes in trading, the higher the expected trading return should be, as well as the required risk capital. The required equity risk capital EXHIBIT 34.15 Value Drivers: Trading Activities (Simplified) Key value drivers 2 Return on VaR1 1 TTrading position: Net trading position (assets minus liabilities) 2 Return on VaR: Relative trading result 3 VaR/net trading assets: Relative trading risk 4 Operating expenses: E.g., driven by number of traders relative to trading assets and bonuses paid out on trading profits 5 Equity: Required equity levels 6 Growth: Growth of trading volumes 7 COE: Cost of equity 1 Trading result Trading liabilities Trading assets Coost/i t n come m 4 Operating expenses1 Return on equity 3 VaR/net trading assets 6 Value creation 5 Growth Capital ratio Equity 7 Cost of equity 1 After taxes. 782 BANKS for the trading activities follows from the VaR (and RWA), as discussed earlier in the chapter.

In December 2010, new regulatory requirements for capital adequacy were specified in the Basel III guidelines, replacing the 2007 Basel II accords, which were no longer considered adequate in the wake of the 2008 and 2010 financial crises.15 The new guidelines are being gradually implemented by banks across the world between 2013 and 2019. 15 The Basel accords are recommendations on laws and regulations for banking and are issued by the Basel Committee on Banking Supervision (BCBS). 778 BANKS EXHIBIT 34.13 Estimating Risk-Weighted Assets (RWA) for a Large European Bank billion Reported RWA Asset category 2013 Loans to countries Loans to banks Loans to corporations Residential mortgages Other consumer loans Overall Operational risk Market risk Credit risk Year Estimated RWA parameters, % Loans outstanding RWA 16,228 25,100 147,242 148,076 45,440 382,086 202,219 Standardized Standardized Allocated RWA/loans RWA RWA 10 35 35 35 75 Year VaR trading book RWA Estimated RWA/ VaR 2013 19,564 47,259 242 Year Revenues RWA Estimated RWA/ revenues 2013 32,826 50,891 155 1,623 8,785 51,535 51,827 34,080 147,489 2,220 12,016 70,486 70,885 46,613 202,219 Estimated RWA/loans 14 48 48 48 103 53 Basel III specifies rules for banks regarding how much equity capital they must hold based on the bank’s so-called risk-weighted assets (RWA).16 The level of RWA is driven by the riskiness of a bank’s asset portfolio and its trading book.


pages: 374 words: 114,600

The Quants by Scott Patterson

Alan Greenspan, Albert Einstein, AOL-Time Warner, asset allocation, automated trading system, Bear Stearns, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Monday: stock market crash in 1987, Black Swan, Black-Scholes formula, Blythe Masters, Bonfire of the Vanities, book value, Brownian motion, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, Carl Icahn, centralized clearinghouse, Claude Shannon: information theory, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, Doomsday Clock, Dr. Strangelove, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial engineering, Financial Modelers Manifesto, fixed income, Glass-Steagall Act, global macro, Gordon Gekko, greed is good, Haight Ashbury, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, index fund, invention of the telegraph, invisible hand, Isaac Newton, Jim Simons, job automation, John Meriwether, John Nash: game theory, junk bonds, Kickstarter, law of one price, Long Term Capital Management, Louis Bachelier, low interest rates, mandelbrot fractal, margin call, Mark Spitznagel, merger arbitrage, Michael Milken, military-industrial complex, money market fund, Myron Scholes, NetJets, new economy, offshore financial centre, old-boy network, Paul Lévy, Paul Samuelson, Ponzi scheme, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, risk-adjusted returns, Robert Mercer, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Savings and loan crisis, Sergey Aleynikov, short selling, short squeeze, South Sea Bubble, speech recognition, statistical arbitrage, The Chicago School, The Great Moderation, The Predators' Ball, too big to fail, transaction costs, value at risk, volatility smile, yield curve, éminence grise

Morgan quants created measured the daily volatility of the firm’s positions and then translated that volatility into a dollar amount. It was a statistical distribution of average volatility based on Brownian motion. Plotted on a graph, that volatility looked like a bell curve. The result was a model they called value-at-risk, or VAR. It was a metric showing the amount of money the bank could lose over a twenty-four-hour period within a 95 percent probability. The powerful VAR radar system had a dangerous allure. If risk could be quantified, it also could be controlled through sophisticated hedging strategies. This belief can be seen in LTCM’s October 1993 prospectus: “The reduction in the Portfolio Company’s volatility through hedging could permit the leveraging up of the resulting position to the same expected level of volatility as an unhedged position, but with a larger expected return.”

When a firm has to sell in a market without buyers, prices run to the extremes beyond the bell curve.” Prices for everything from stocks to currencies to bonds held by LTCM moved in a bizarre fashion that defied logic. LTCM had relied on complex hedging strategies, massive hairballs of derivatives, and risk management tools such as VAR to allow it to leverage up to the maximum amount possible. By carefully hedging its holdings, LTCM could reduce its capital, otherwise known as equity. That freed up cash to make other bets. As Myron Scholes explained before the disaster struck: “I like to think of equity as an all-purpose risk cushion.

But the losses were piling up rapidly and soon topped $1 billion. He pleaded with Deutsche’s risk managers to let him purchase more swaps so he could better hedge his positions, but the word had come down from on high: buying wasn’t allowed, only selling. Perversely, the bank’s risk models, such as the notorious VAR used by all Wall Street banks, instructed traders to exit short positions, including credit default swaps. Weinstein knew that was crazy, but the quants in charge of risk couldn’t be argued with. “Step away from the model,” he begged. “The only way for me to get out of this is to be short. If the market is falling and you’re losing money, that means you are long the market—and you need to short it, as fast as possible.”


pages: 289 words: 95,046

Chaos Kings: How Wall Street Traders Make Billions in the New Age of Crisis by Scott Patterson

"World Economic Forum" Davos, 2021 United States Capitol attack, 4chan, Alan Greenspan, Albert Einstein, asset allocation, backtesting, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, bitcoin, Bitcoin "FTX", Black Lives Matter, Black Monday: stock market crash in 1987, Black Swan, Black Swan Protection Protocol, Black-Scholes formula, blockchain, Bob Litterman, Boris Johnson, Brownian motion, butterfly effect, carbon footprint, carbon tax, Carl Icahn, centre right, clean tech, clean water, collapse of Lehman Brothers, Colonization of Mars, commodity super cycle, complexity theory, contact tracing, coronavirus, correlation does not imply causation, COVID-19, Credit Default Swap, cryptocurrency, Daniel Kahneman / Amos Tversky, decarbonisation, disinformation, diversification, Donald Trump, Doomsday Clock, Edward Lloyd's coffeehouse, effective altruism, Elliott wave, Elon Musk, energy transition, Eugene Fama: efficient market hypothesis, Extinction Rebellion, fear index, financial engineering, fixed income, Flash crash, Gail Bradbrook, George Floyd, global pandemic, global supply chain, Gordon Gekko, Greenspan put, Greta Thunberg, hindsight bias, index fund, interest rate derivative, Intergovernmental Panel on Climate Change (IPCC), Jeff Bezos, Jeffrey Epstein, Joan Didion, John von Neumann, junk bonds, Just-in-time delivery, lockdown, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, Mark Spitznagel, Mark Zuckerberg, market fundamentalism, mass immigration, megacity, Mikhail Gorbachev, Mohammed Bouazizi, money market fund, moral hazard, Murray Gell-Mann, Nick Bostrom, off-the-grid, panic early, Pershing Square Capital Management, Peter Singer: altruism, Ponzi scheme, power law, precautionary principle, prediction markets, proprietary trading, public intellectual, QAnon, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Nader, Ralph Nelson Elliott, random walk, Renaissance Technologies, rewilding, Richard Thaler, risk/return, road to serfdom, Ronald Reagan, Ronald Reagan: Tear down this wall, Rory Sutherland, Rupert Read, Sam Bankman-Fried, Silicon Valley, six sigma, smart contracts, social distancing, sovereign wealth fund, statistical arbitrage, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, systematic trading, tail risk, technoutopianism, The Chicago School, The Great Moderation, the scientific method, too big to fail, transaction costs, University of East Anglia, value at risk, Vanguard fund, We are as Gods, Whole Earth Catalog

Neil Chriss, a cerebral former Goldman Sachs quant, had started a program at NYU in applied mathematical finance, among the first of its kind. Chriss admired Taleb’s trading tome, Dynamic Hedging, and hired him as an adjunct professor. The name of Taleb’s course was Model Failure in Quantitative Finance. One of his biggest complaints, he told his students, was with a widely used metric at banks called VaRvalue at risk. It was a broad measurement of the risk of a bank’s portfolio, of its exposure to extreme losses. Created by a group of math whizzes at J.P. Morgan and elsewhere on Wall Street in the late 1980s and early 1990s, the metric measured how much the assets in a portfolio had moved up or down historically, with various tweaks for how much the assets were correlated with one another (e.g., bonds and gold often move in the same direction, safe-haven utilities and risky tech stocks go in the opposite direction).

Mandelbrot had argued—for decades—that the traditional models used in finance that depend on Gaussian mathematics—i.e., bell curves—were highly flawed. Deployed by nineteenth-century mathematician Carl Friedrich Gauss for astronomical measurements, the bell curve (commonly known as the Gaussian) had become ubiquitous in financial and economic models such as Value at Risk. It measured phenomena that had smooth step-by-step transitions, with most samples falling within the safe confines of the middle of the bell curve. The bell curve didn’t capture the extreme volatility that can occur in a fractal world—the world of power laws, sudden jumps, wild leaps. Much of Mandelbrot’s work was based on power laws driving all sorts of phenomena, from cotton prices to income distributions to population densities in cities.

Paulson and Burry had identified a systemic weakness in the U.S. housing market and had devised an ingenious way to profit when it blew up. Spitznagel and Taleb, by contrast, had identified weaknesses in the financial system as a whole. The widespread use of derivatives, the application of faulty risk-management tools such as value at risk, the explosion of leverage, the Federal Reserve’s lax monetary policy, the transformation of the investment banking industry into a drunken Wild West hedge fund−mad casino—all had turned the global financial system into a house of cards. Few shared their views, and that meant they could make their bet—like Paulson’s and Burry’s swaps—on the cheap.


Alpha Trader by Brent Donnelly

Abraham Wald, algorithmic trading, Asian financial crisis, Atul Gawande, autonomous vehicles, backtesting, barriers to entry, beat the dealer, behavioural economics, bitcoin, Boeing 747, buy low sell high, Checklist Manifesto, commodity trading advisor, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, cryptocurrency, currency manipulation / currency intervention, currency risk, deep learning, diversification, Edward Thorp, Elliott wave, Elon Musk, endowment effect, eurozone crisis, fail fast, financial engineering, fixed income, Flash crash, full employment, global macro, global pandemic, Gordon Gekko, hedonic treadmill, helicopter parent, high net worth, hindsight bias, implied volatility, impulse control, Inbox Zero, index fund, inflation targeting, information asymmetry, invisible hand, iterative process, junk bonds, Kaizen: continuous improvement, law of one price, loss aversion, low interest rates, margin call, market bubble, market microstructure, Market Wizards by Jack D. Schwager, McMansion, Monty Hall problem, Network effects, nowcasting, PalmPilot, paper trading, pattern recognition, Peter Thiel, prediction markets, price anchoring, price discovery process, price stability, quantitative easing, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, reserve currency, risk tolerance, Robert Shiller, secular stagnation, Sharpe ratio, short selling, side project, Stanford marshmallow experiment, Stanford prison experiment, survivorship bias, tail risk, TED Talk, the scientific method, The Wisdom of Crowds, theory of mind, time dilation, too big to fail, transaction costs, value at risk, very high income, yield curve, you are the product, zero-sum game

The way you answer this question might be the difference between long-term success and instant, nuclear devastation of your trading account. See GameStop shorts, for example. 3. Maximum position size by product. 4. Value at risk (VAR) or stress-test P&L. These are higher-level frameworks used at banks and hedge funds, but both methods have many issues when applied in real life. If you trade from home, don’t worry about VAR, it’s a tool used by institutions. Some problems with VAR and stress testing: » VAR often depends on historical volatility which is not always a good predictor of future volatility. » Stress tests can overweight rare events and ignore the fact that stop losses are effective loss control mechanisms in liquid markets.

., 248, 349, 492 Tierney, John, 56—57, 106, 491 tight/aggressive trading style, 138, 197 time dilation, 189, 483 time of day/week/month bid/offer spread, 278 FX trades and, 312 as market catalyst, 310, 312 volume, 283—286, 288 Tolle, Eckhart, 128—129, 158, 491 Toronto Stock Exchange, 473 tracking, trade, 392, 423 trade, lifecycle of hypothetical, 392—424 execution, 392, 418—423 filtering, 392, 394, 407—411 grid showing, 392 idea generation, 392, 393—407 performance analysis, 392, 423—424 position sizing, 392, 417 tactics, 392, 412—416 tracking, 392, 423 trade, lifecycle of short EURUSD (Sept 2020), 425—435 EURUSD hourly chart Aug and Sept 2020 and, 429, 430 execution, 433—434 filtering, 431—432 idea generation, 431 overview, 425—431 position sizing, 432—433 as stale narrative trade, 431 tactics, 432 trade quotas, 113 trader attributes, positive cognitive, 75—104 confidence, calibrated, 75, 81 creative, 75, 86—89 curious, 75, 89—90 flexible and open-minded, 75, 83—86 future-oriented, 75 grit/perseverance/mental endurance, 75, 91—94 growth mindset, 75, 90—91 healthy skeptic, 103—104 independent thinker, 75, 82—83 intelligent/knowledgeable/informed, 75, 77—78 organized/meticulous, 75, 95—100 process-oriented, 75, 100—103 quantitative, 75, 78—81 rational, 75, 76 trader attributes, positive miscellaneous, 75, 143—154 appropriate capital, 75, 143, 148—150 experience, 75, 143, 146 healthy and supportive risk-taking environment, 75, 143, 148, 150-154 luck, 75, 143, 146-147 passion for trading, 75, 143, 144—146 raw trading instinct, 75, 143—144 trader attributes, positive non-cognitive, 75, 104—142 ability to manage stress, 75, 104, 131—135 adaptable, 75, 104, 106—107 courageous, 75, 104, 107—119 decisive, 119—121, 484 disciplined/self-control, 75, 104, 105—106 focused, 75, 104, 135—138, 484 locus of control understanding, 75, 104, 139—142 patient, 75, 104, 138—139 self-aware, 75, 104, 121—131 Trader Self-Assessment, 30—37 traders, evaluating, 101 See also specific types of traders traders, traits of excellent, 493 See also trader attributes, positive cognitive; trader attributes, positive miscellaneous; trader attributes, positive non-cognitive traders, traits of losing, 494 trades, evaluating, 101 trading gambling and, 191—192 information asymmetry, 156 as negative sum game, 39, 155, 156, 277 quitting, 145—146 rules, 101, 467 technology asymmetry, 156 See also trading costs; trading methods; strategy, trading; transaction costs trading costs, 155, 156 See also transaction costs trading history, personal, 461—462 trading instinct, raw as trader attribute, 75, 143—144 as x-factor, 143 trading journal, 99—100 building self-awareness with, 121—126 improving trading process with, 424 monthly review, 124 recording trade executions in, 392, 422 sample, 123 takeaway goals, 125 trading self-awareness and, 124 trading methods adapting to new, 449—451 varying, 467 See also strategy, trading transaction costs, 40, 70, 155, 276, 277—279, 418-422 bid/offer, 155 commissions, 155 day trader P&L before and after, 279 FX trader P&L before and after, 279 how to reduce, 418—422 slippage, 155 trend following, 159—160, 173 trend indicators moving averages, 337—338 trend reversal indicator, 312 trending markets, 448 identifying, 448, 449 Turek, Jon, 492 Turnaround Tuesday, 222, 223—225 anatomy of a sell-off, 223 total cumulative performance of Tuesdays in 2008, 225 Tuesday 1-day S&P 500 return, 224 Tversky, Amos, 198 TWAP order, 419, 420 Twitter, 78, 83, 98 bias, 235, 236 confirmation bias on, 198 information and analysis source, 156 finance experts on, 308 monitoring, 35 real-time news from, 230 sentiment, 267 sharing hypotheses on, 84 stock market crash speculation on, 264 USDJPY Hourly, April to July 2020, 221 May 3—7, 2010, 18, 485 vs. US 10-year rates. Nov 2009 to May 5, 2010, 14, 480 Value at risk (VAR), 118 Van Witteloostuijn, Arjen, 70 Vantage Point Trading, 40—41 variance, 117, 141, 362—367 accepting, 367, 467 bank trader simulation, 363, 364 in life, 454 losing money and, 147 monthly risk management and, 362—367 poker and, 363 retail trader simulation, 363, 364 short-term, 54 versus bad decision making, 365—367 versus luck, 146 VIX.

Some problems with VAR and stress testing: » VAR often depends on historical volatility which is not always a good predictor of future volatility. » Stress tests can overweight rare events and ignore the fact that stop losses are effective loss control mechanisms in liquid markets. For example, stress-testing the P&L of 50 million USDJPY against five years of data might show a maximum possible loss of 500 pips or $2.5 million (approx.) but a stop loss will protect a disciplined trader from anywhere near that large of a loss. » VAR and stress testing often rely on historical correlation which is backward-looking and unstable. This book is for active, short-term traders attempting to generate alpha. It is not meant to address carry, short volatility, rolldown, basis, and other trading strategies that involve convex and non-linear payout structures. 5.


pages: 992 words: 292,389

Conspiracy of Fools: A True Story by Kurt Eichenwald

"World Economic Forum" Davos, Alan Greenspan, Asian financial crisis, Bear Stearns, book value, Burning Man, California energy crisis, computerized trading, corporate raider, currency risk, deal flow, electricity market, estate planning, financial engineering, forensic accounting, intangible asset, Irwin Jacobs, John Markoff, junk bonds, Long Term Capital Management, margin call, Michael Milken, Negawatt, new economy, oil shock, price stability, pushing on a string, Ronald Reagan, transaction costs, value at risk, young professional

Now, the fluctuations in California were playing havoc with the formula, known as value at risk, or VAR. One perverse effect was that even if the traders stopped trading, they still might hit the risk limits. Whalley called Skilling to let him know the dilemma. The directors would need to kick up the VAR limit by about 30 percent to maintain current positions, he said. “No problem,” Skilling said. “I’ll take care of it.” This was just administrative, Skilling figured. He telephoned Pug Winokur, the head of the finance committee. “We’re going to need to make a request to the board for additional VAR,” Skilling said, giving the 30 percent estimate.

“I just got a call from John Duncan about you wanting to get a VAR increase,” he said. “What’s going on?” John Duncan? Why was the head of the board’s executive committee calling Lay? “I don’t know what’s going on, Ken,” Skilling said. “I told Pug we were going to ask for an increase in VAR. It’s just a mathematical function, because of the increase in volatility.” Lay sat down. “Well, there’s something else going on. I mean, the directors are all talking among themselves.” This is ridiculous. “Ken, the position we have in VAR is just one-tenth of the risk we were taking in India. We’ve gone through hoops to tell them how VAR works. But they can approve a project in India in a twenty-two-minute phone call.

This wasn’t the collaborative effort, the chance to expound on his views that he had been hoping for. These lawyers clearly thought he was responsible. He couldn’t believe it. “Would it surprise you to find out Fastow made about thirty-five million dollars from LJM in the last two years?” McLucas asked. Skilling shrugged. “Depends on what he had at risk,” he said. “You guys should do a value-at-risk analysis.” They haggled over the LJM approval sheets. Fastow had told the board that Skilling was approving each deal, McLucas said. That wasn’t the process, Skilling retorted. Only Causey and Buy were formally meant to approve each deal. McLucas brought out an approval sheet for a deal named Margaux, the sole LJM transaction signed by Skilling.


pages: 368 words: 32,950

How the City Really Works: The Definitive Guide to Money and Investing in London's Square Mile by Alexander Davidson

accounting loophole / creative accounting, algorithmic trading, asset allocation, asset-backed security, bank run, banking crisis, barriers to entry, Bear Stearns, Big bang: deregulation of the City of London, buy and hold, capital asset pricing model, central bank independence, corporate governance, Credit Default Swap, currency risk, dematerialisation, discounted cash flows, diversified portfolio, double entry bookkeeping, Edward Lloyd's coffeehouse, Elliott wave, equity risk premium, Exxon Valdez, foreign exchange controls, forensic accounting, Glass-Steagall Act, global reserve currency, high net worth, index fund, inflation targeting, information security, intangible asset, interest rate derivative, interest rate swap, inverted yield curve, John Meriwether, junk bonds, London Interbank Offered Rate, Long Term Capital Management, low interest rates, margin call, market fundamentalism, Nick Leeson, North Sea oil, Northern Rock, pension reform, Piper Alpha, price stability, proprietary trading, purchasing power parity, Real Time Gross Settlement, reserve currency, Right to Buy, risk free rate, shareholder value, short selling, The Wealth of Nations by Adam Smith, transaction costs, value at risk, yield curve, zero-coupon bond

Problems and fraud The problem with derivatives is not in the product itself, but in how it is sold or managed. If a company is to trade in derivatives, it must understand their value. Software data will calculate the value at risk, known as VAR, which is how much the company is willing to lose at any time. The VAR changes daily. Banks have thousands of loans on their books, both receiving and giving. They  68 HOW THE CITY REALLY WORKS __________________________________ need good systems and procedures to determine VAR, and this is an underlying complexity. There are always rogue traders, or treasurers of companies who do not behave responsibly. An overzealous derivatives salesman could go to an unaware company treasurer and say: ‘Swap your fixed rate for a variable rate loan.

Index 419 fraud 204 9/11 terrorist attacks 31, 218, 242, 243, 254, 257 Abbey National 22 ABN AMRO 103 accounting and governance 232–38 scandals 232 Accounting Standards Board (ASB) 236 administration 17 Allianz 207 Alternative Investment Market (AIM) 44–45, 131, 183, 238 Amaranth Advisors 170 analysts 172–78 fundamental 172–74 others 177–78 Spitzer impact 174–75 technical 175–77 anti-fraud agencies Assets Recovery Agency 211–13 City of London Police 209 Financial Services Authority 208 Financial Crime and Intelligence Division 208 Insurance Fraud Bureau 209 Insurance Fraud Investigators Group 209 International Association of Insurance Fraud Agencies 207, 210, 218 National Criminal Intelligence Service 210 Serious Fraud Office 213–15 Serious Organised Crime Agency 210–11 asset finance 24–25 Association of Investment Companies 167 backwardation 101 bad debt, collection of 26–28 Banco Santander Central Hispano 22 Bank for International Settlements (BIS) 17, 27, 85, 98, 114 bank guarantee 23 Bank of Credit and Commerce International (BCCI) 10, 214 Bank of England 6, 10–17 Court of the 11 credit risk warning 98 framework for sterling money markets 81 Governor 11, 13, 14 history 10, 15–16 Inflation Report 14 inflation targeting 12–13 interest rates and 12 international liaison 17 lender of last resort 15–17 Market Abuse Directive (MAD) 16 monetary policy and 12–15 Monetary Policy Committee (MPC) 13–14 Open-market operations 15, 82 repo rate 12, 15 role 11–12 RTGS (Real Time Gross Settlement) 143 statutory immunity 11 supervisory role 11 Bank of England Act 1988 11, 12 Bank of England Quarterly Model (BEQM) 14 Banking Act 1933 see Glass-Steagall Act banks commercial 5 investment 5 Barclays Bank 20 Barings 11, 15, 68, 186, 299 Barlow Clowes case 214 Barron’s 99 base rate see repo rate Basel Committee for Banking Supervision (BCBS) 27–28 ____________________________________________________ INDEX 303 Basel I 27 Basel II 27–28, 56 Bear Stearns 95, 97 BearingPoint 97 bill of exchange 26 Bingham, Lord Justice 10–11 Blue Arrow trial 214 BNP Paribas 145, 150 bond issues see credit products book runners 51, 92 Borsa Italiana 8, 139 bps 90 British Bankers’ Association 20, 96, 97 building societies 22–23 demutualisation 22 Building Societies Association 22 Capital Asset Pricing Model (CAPM) see discounted cash flow analysis capital gains tax 73, 75, 163, 168 capital raising markets 42–46 mergers and acquisitions (M&A) 56–58 see also flotation, bond issues Capital Requirements Directive 28, 94 central securities depository (CSD) 145 international (ICSD) 145 Central Warrants Trading Service 73 Chancellor of the Exchequer 12, 13, 229 Chicago Mercantile Exchange 65 Citigroup 136, 145, 150 City of London 4–9 Big Bang 7 definition 4 employment in 8–9 financial markets 5 geography 4–5 history 6–7 services offered 4 world leader 5–6 clearing 140, 141–42 Clearing House Automated Payment System (CHAPS) 143 Clearstream Banking Luxembourg 92, 145 commercial banking 5, 18–28 bad loans and capital adequacy 26–28 banking cards 21 building societies 22–23 credit collection 25–26 finance raising 23–25 history 18–19 overdrafts 23 role today 19–21 commodities market 99–109 exchange-traded commodities 101  fluctuations 100 futures 100 hard commodities energy 102 non-ferrous metals 102–04 precious metal 104–06 soft commodities cocoa 107 coffee 106 sugar 107 Companies Act 2006 204, 223, 236 conflict of interests 7 consolidation 138–39 Consumer Price Index (CPI) 13 contango 101 Continuous Linked Settlement (CLS) 119 corporate governance 223–38 best practice 231 Cadbury Code 224 Combined Code 43, 225 compliance 230 definition 223 Directors’ Remuneration Report Regulations 226 EU developments 230 European auditing rules 234–35 Greenbury Committee 224–25 Higgs and Smith reports 227 International Financial Reporting Standards (IFRS) 237–38 Listing Rules 228–29 Model Code 229 Myners Report 229 OECD Principles 226 operating and financial review (OFR) 235– 36 revised Combined Code 227–28 Sarbanes–Oxley Act 233–34 Turnbull Report 225 credit cards 21 zero-per-cent cards 21 credit collection 25–26 factoring and invoice discounting 26 trade finance 25–26 credit derivatives 96–97 back office issues 97 credit default swap (CDS) 96–97 credit products asset-backed securities 94 bonds 90–91 collateralised debt obligations 94–95 collateralised loan obligation 95 covered bonds 93 equity convertibles 93 international debt securities 92–93  304 INDEX ____________________________________________________ junk bonds 91 zero-coupon bonds 93 credit rating agencies 91 Credit Suisse 5, 136, 193 CREST system 141, 142–44 dark liquidity pools 138 Debt Management Office 82, 86 Department of Trade and Industry (DTI) 235, 251, 282 derivatives 60–77 asset classes 60 bilateral settlement 66 cash and 60–61 central counterparty clearing 65–66 contracts for difference 76–77, 129 covered warrants 72–73 futures 71–72 hedging and speculation 67 on-exchange vs OTC derivatives 63–65 options 69–71 Black-Scholes model 70 call option 70 equity option 70–71 index options 71 put option 70 problems and fraud 67–68 retail investors and 69–77 spread betting 73–75 transactions forward (future) 61–62 option 62 spot 61 swap 62–63 useful websites 75 Deutsche Bank 136 Deutsche Börse 64, 138 discounted cash flow analysis (DCF) 39 dividend 29 domestic financial services complaint and compensation 279–80 financial advisors 277–78 Insurance Mediation Directive 278–79 investments with life insurance 275–76 life insurance term 275 whole-of-life 274–75 NEWICOB 279 property and mortgages 273–74 protection products 275 savings products 276–77 Dow theory 175 easyJet 67 EDX London 66 Egg 20, 21 Elliott Wave Theory 176 Enron 67, 114, 186, 232, 233 enterprise investment schemes 167–68 Equiduct 133–34, 137 Equitable Life 282 equities 29–35 market indices 32–33 market influencers 40–41 nominee accounts 31 shares 29–32 stockbrokers 33–34 valuation 35–41 equity transparency 64 Eurex 64, 65 Euro Overnight Index Average (EURONIA) 85 euro, the 17, 115 Eurobond 6, 92 Euroclear Bank 92, 146, 148–49 Euronext.liffe 5, 60, 65, 71 European Central Bank (ECB) 16, 17, 84, 148 European Central Counterparty (EuroCCP) 136 European Code of Conduct 146–47, 150 European Exchange Rate Mechanism 114 European Harmonised Index of Consumer Prices 13 European Union Capital Requirements Directive 199 Market Abuse Directive (MAD) 16, 196 Market in Financial Instruments Directive (MiFID) 64, 197–99 Money Laundering Directive 219 Prospectus Directive 196–97 Transparency Directive 197 exchange controls 6 expectation theory 172 Exxon Valdez 250 factoring see credit collection Factors and Discounters Association 26 Fair & Clear Group 145–46 Federal Deposit Insurance Corporation 17 Federation of European Securities Exchanges 137 Fighting Fraud Together 200–01 finance, raising 23–25 asset 24–25 committed 23 project finance 24 recourse loan 24 syndicated loan 23–24 uncommitted 23 Financial Action Task Force on Money Laundering (FATF) 217–18 financial communications 179–89 ____________________________________________________ INDEX 305 advertising 189 corporate information flow 185 primary information providers (PIPs) 185 investor relations 183–84 journalists 185–89 public relations 179–183 black PR’ 182–83 tipsters 187–89 City Slickers case 188–89 Financial Ombudsman Service (FOS) 165, 279–80 financial ratios 36–39 dividend cover 37 earnings per share (EPS) 36 EBITDA 38 enterprise multiple 38 gearing 38 net asset value (NAV) 38 price/earnings (P/E) 37 price-to-sales ratio 37 return on capital employed (ROCE) 38 see also discounted cash flow analysis Financial Reporting Council (FRC) 224, 228, 234, 236 Financial Services Act 1986 191–92 Financial Services Action Plan 8, 195 Financial Services and Markets Act 2001 192 Financial Services and Markets Tribunal 94 Financial Services Authority (FSA) 5, 8, 31, 44, 67, 94, 97, 103, 171, 189, 192–99 competition review 132 insurance industry 240 money laundering and 219 objectives 192 regulatory role 192–95 powers 193 principles-based 194–95 Financial Services Compensation Scheme (FSCS) 17, 165, 280 Financial Services Modernisation Act 19 financial services regulation 190–99 see also Financial Services Authority Financial Times 9, 298 First Direct 20 flipping 53 flotation beauty parade 51 book build 52 early secondary market trading 53 grey market 52, 74 initial public offering (IPO) 47–53 pre-marketing 51–52 pricing 52–53 specialist types of share issue accelerated book build 54  bought deal 54 deeply discounted rights issue 55 introduction 55 placing 55 placing and open offer 55 rights issues 54–55 underwriting 52 foreign exchange 109–120 brokers 113 dealers 113 default risk 119 electronic trading 117 exchange rate 115 ICAP Knowledge Centre 120 investors 113–14 transaction types derivatives 116–17 spot market 115–16 Foreign Exchange Joint Standing Committee 112 forward rate agreement 85 fraud 200–15 advanced fee frauds 204–05 boiler rooms 201–04 Regulation S 202 future regulation 215 identity theft 205–06 insurance fraud 206–08 see also anti-fraud agencies Fraud Act 2006 200 FTSE 100 32, 36, 58, 122, 189, 227, 233 FTSE 250 32, 122 FTSE All-Share Index 32, 122 FTSE Group 131 FTSE SmallCap Index 32 FTSE Sterling Corporate Bond Index 33 Futures and Options Association 131 Generally Accepted Accounting Principles (GAAP) 237, 257 gilts 33, 86–88 Giovanni Group 146 Glass-Steagall Act 7, 19 Global Bond Market Forum 64 Goldman Sachs 136 government bonds see gilts Guinness case 214 Halifax Bank 20 hedge funds 8, 77, 97, 156–57 derivatives-based arbitrage 156 fixed-income arbitrage 157 Hemscott 35 HM Revenue and Customs 55, 211 HSBC 20, 103 Hurricane Hugo 250  306 INDEX ____________________________________________________ Hurricane Katrina 2, 67, 242 ICE Futures 5, 66, 102 Individual Capital Adequacy Standards (ICAS) 244 inflation 12–14 cost-push 12 definition 12 demand-pull 12 quarterly Inflation Report 14 initial public offering (IPO) 47–53 institutional investors 155–58 fund managers 155–56 hedge fund managers 156–57 insurance companies 157 pension funds 158 insurance industry London and 240 market 239–40 protection and indemnity associations 241 reform 245 regulation 243 contingent commissions 243 contract certainty 243 ICAS and Solvency II 244–45 types 240–41 underwriting process 241–42 see also Lloyd’s of London, reinsurance Intercontinental Exchange 5 interest equalisation tax 6 interest rate products debt securities 82–83, 92–93 bill of exchange 83 certificate of deposit 83 debt instrument 83 euro bill 82 floating rate note 83 local authority bill 83 T-bills 82 derivatives 85 forward rate agreements (FRAs) 85–86 government bonds (gilts) 86–89 money markets 81–82 repos 84 International Financial Reporting Standards (IFRS) 58, 86, 173, 237–38 International Financial Services London (IFSL) 5, 64, 86, 92, 112 International Monetary Fund 17 International Securities Exchange 138 International Swap Dealers Association 63 International Swaps and Derivatives Association 63 International Underwriting Association (IUA) 240 investment banking 5, 47–59 mergers and acquisitions (M&A) 56–58 see also capital raising investment companies 164–69 real estate 169 split capital 166–67 venture capital 167–68 investment funds 159–64 charges 163 investment strategy 164 fund of funds scheme 164 manager-of-managers scheme 164 open-ended investment companies (OEICs) 159 selection criteria 163 total expense ratio (TER) 164 unit trusts 159 Investment Management Association 156 Investment Management Regulatory Organisation 11 Johnson Matthey Bankers Limited 15–16 Joint Money Laundering Steering Group 221 KAS Bank 145 LCH.Clearnet Limited 66, 140 letter of credit (LOC) 23, 25–26 liability-driven investment 158 Listing Rules 43, 167, 173, 225, 228–29 Lloyd’s of London 8, 246–59 capital backing 249 chain of security 252–255 Central Fund 253 Corporation of Lloyd’s 248–49, 253 Equitas Reinsurance Ltd 251, 252, 255–56 Franchise Performance Directorate 256 future 258–59 Hardship Committee 251 history 246–47, 250–52 international licenses 258 Lioncover 252, 256 Member’s Agent Pooling Arrangement (MAPA) 249, 251 Names 248, one-year accounting 257 regulation 257 solvency ratio 255 syndicate capacity 249–50 syndicates 27 loans 23–24 recourse loan 24 syndicated loan 23–24 London Interbank Offered Rate (LIBOR) 74, 76 ____________________________________________________ INDEX 307 London Stock Exchange (LSE) 7, 8, 22, 29, 32, 64 Alternative Investment Market (AIM) 32 Main Market 42–43, 55 statistics 41 trading facilities 122–27 market makers 125–27 SETSmm 122, 123, 124 SETSqx 124 Stock Exchange Electronic Trading Service (SETS) 122–25 TradElect 124–25 users 127–29 Louvre Accord 114 Markets in Financial Instruments Directive (MiFID) 64, 121, 124, 125, 130, 144, 197–99, 277 best execution policy 130–31 Maxwell, Robert 186, 214, 282 mergers and acquisitions 56–58 current speculation 57–58 disclosure and regulation 58–59 Panel on Takeovers and Mergers 57 ‘white knight’ 57 ‘white squire’ 57 Merrill Lynch 136, 174, 186, 254 money laundering 216–22 Egmont Group 218 hawala system 217 know your client (KYC) 217, 218 size of the problem 222 three stages of laundering 216 Morgan Stanley 5, 136 multilateral trading facilities Chi-X 134–35, 141 Project Turquoise 136, 141 Munich Re 207 Nasdaq 124, 138 National Strategy for Financial Capability 269 National Westminster Bank 20 Nationwide Building Society 221 net operating cash flow (NOCF) see discounted cash flow analysis New York Federal Reserve Bank (Fed) 16 Nomads 45 normal market share (NMS) 132–33 Northern Rock 16 Nymex Europe 102 NYSE Euronext 124, 138, 145 options see derivatives Oxera 52  Parmalat 67, 232 pensions alternatively secured pension 290 annuities 288–89 occupational pension final salary scheme 285–86 money purchase scheme 286 personal account 287 personal pension self-invested personal pension 288 stakeholder pension 288 state pension 283 unsecured pension 289–90 Pensions Act 2007 283 phishing 200 Piper Alpha oil disaster 250 PLUS Markets Group 32, 45–46 as alternative to LSE 45–46, 131–33 deal with OMX 132 relationship to Ofex 46 pooled investments exchange-traded funds (ETF) 169 hedge funds 169–71 see also investment companies, investment funds post-trade services 140–50 clearing 140, 141–42 safekeeping and custody 143–44 registrar services 144 settlement 140, 142–43 real-time process 142 Proceeds of Crime Act 2003 (POCA) 211, 219, 220–21 Professional Securities Market 43–44 Prudential 20 purchasing power parity 118–19 reinsurance 260–68 cat bonds 264–65 dispute resolution 268 doctrines 263 financial reinsurance 263–64 incurred but not reported (IBNR) claims insurance securitisation 265 non-proportional 261 offshore requirements 267 proportional 261 Reinsurance Directive 266–67 retrocession 262 types of contract facultative 262 treaty 262 retail banking 20 retail investors 151–155 Retail Prices Index (RPI) 13, 87 264  308 INDEX ____________________________________________________ Retail Service Provider (RSP) network Reuters 35 Royal Bank of Scotland 20, 79, 221 73 Sarbanes–Oxley Act 233–34 securities 5, 29 Securities and Futures Authority 11 self-regulatory organisations (SROs) 192 Serious Crime Bill 213 settlement 11, 31, 140, 142–43 shareholder, rights of 29 shares investment in 29–32 nominee accounts 31 valuation 35–39 ratios 36–39 see also flotation short selling 31–32, 73, 100, 157 Society for Worldwide Interbank Financial Telecommunications (SWIFT) 119 Solvency II 244–245 Soros, George 114, 115 Specialist Fund Market 44 ‘square mile’ 4 stamp duty 72, 75, 166 Sterling Overnight Index Average (SONIA) 85 Stock Exchange Automated Quotation System (SEAQ) 7, 121, 126 Stock Exchange Electronic Trading Service (SETS) see Lloyd’s of London stock market 29–33 stockbrokers 33–34 advisory 33 discretionary 33–34 execution-only 34 stocks see shares sub-prime mortgage crisis 16, 89, 94, 274 superequivalence 43 suspicious activity reports (SARs) 212, 219–22 swaps market 7 interest rates 56 swaptions 68 systematic internalisers (SI) 137–38 Target2-Securities 147–48, 150 The Times 35, 53, 291 share price tables 36–37, 40 tip sheets 33 trading platforms, electronic 80, 97, 113, 117 tranche trading 123 Treasury Select Committee 14 trend theory 175–76 UBS Warburg 103, 136 UK Listing Authority 44 Undertakings for Collective Investments in Transferable Securities (UCITS) 156 United Capital Asset Management 95 value at risk (VAR) virtual banks 20 virt-x 140 67–68 weighted-average cost of capital (WACC) see discounted cash flow analysis wholesale banking 20 wholesale markets 78–80 banks 78–79 interdealer brokers 79–80 investors 79 Woolwich Bank 20 WorldCom 67, 232 Index of Advertisers Aberdeen Asset Management PLC xiii–xv Birkbeck University of London xl–xlii BPP xliv–xlvi Brewin Dolphin Investment Banking 48–50 Cass Business School xxi–xxiv Cater Allen Private Bank 180–81 CB Richard Ellis Ltd 270–71 CDP xlviii–l Charles Schwab UK Ltd lvi–lviii City Jet Ltd x–xii The City of London inside front cover EBS Dealing Resource International 110–11 Edelman xx ESCP-EAP European School of Management vi ICAS (The Inst. of Chartered Accountants of Scotland) xxx JP Morgan Asset Management 160–62 London Business School xvi–xviii London City Airport vii–viii Morgan Lewis xxix Securities & Investments Institute ii The Share Centre 30, 152–54 Smithfield Bar and Grill lii–liv TD Waterhouse xxxii–xxxiv University of East London xxxvi–xxxviii


pages: 402 words: 110,972

Nerds on Wall Street: Math, Machines and Wired Markets by David J. Leinweber

"World Economic Forum" Davos, AI winter, Alan Greenspan, algorithmic trading, AOL-Time Warner, Apollo 11, asset allocation, banking crisis, barriers to entry, Bear Stearns, Big bang: deregulation of the City of London, Bob Litterman, book value, business cycle, butter production in bangladesh, butterfly effect, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, Charles Babbage, citizen journalism, collateralized debt obligation, Cornelius Vanderbilt, corporate governance, Craig Reynolds: boids flock, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Danny Hillis, demand response, disintermediation, distributed generation, diversification, diversified portfolio, electricity market, Emanuel Derman, en.wikipedia.org, experimental economics, fake news, financial engineering, financial innovation, fixed income, Ford Model T, Gordon Gekko, Hans Moravec, Herman Kahn, implied volatility, index arbitrage, index fund, information retrieval, intangible asset, Internet Archive, Ivan Sutherland, Jim Simons, John Bogle, John Nash: game theory, Kenneth Arrow, load shedding, Long Term Capital Management, machine readable, machine translation, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, market fragmentation, market microstructure, Mars Rover, Metcalfe’s law, military-industrial complex, moral hazard, mutually assured destruction, Myron Scholes, natural language processing, negative equity, Network effects, optical character recognition, paper trading, passive investing, pez dispenser, phenotype, prediction markets, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Reminiscences of a Stock Operator, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Metcalfe, Ronald Reagan, Rubik’s Cube, Savings and loan crisis, semantic web, Sharpe ratio, short selling, short squeeze, Silicon Valley, Small Order Execution System, smart grid, smart meter, social web, South Sea Bubble, statistical arbitrage, statistical model, Steve Jobs, Steven Levy, stock buybacks, Tacoma Narrows Bridge, the scientific method, The Wisdom of Crowds, time value of money, tontine, too big to fail, transaction costs, Turing machine, two and twenty, Upton Sinclair, value at risk, value engineering, Vernor Vinge, Wayback Machine, yield curve, Yogi Berra, your tax dollars at work

Variants on the chromosomes5 used for the forecasting models, as seen in Figure 8.4, allowed for higher levels of flexibility. The simplest Basic—Variables fixed in advance AVG1 SBP gene LAG1 AVG2 LAG2 AVG1 BRP gene LAG1 AVG2 LAG2 Snappy Version—Variables and transforms coded VAR ID X FORM AVG1 LAG1 AVG2 LAG2 VAR ID Really Snappy Version—As above, plus variation in algebraic form PRED ID PRED ID OP VAR ID X FORM VAR ID X FORM AVG1 AVG1 LAG1 LAG1 AVG2 AVG2 LAG2 LAG2 Figure 8.4 Chromosomes for Global Tactical Asset Allocation (GTAA) and Tactical Currency Allocation (TCA) Models Perils and Pr omise of Evolutionary Computation on Wall Str eet 193 chromosomes assumed that the standard predictor variables used in the existing models were utilized, and only the transforms were adjusted.

The next step was to incorporate the idea of risk aversion, and the distinction between passive and alpha-seeking trades. Robert Almgren and Neil Chriss made that major step in their 2000 paper “Optimal Execution of Portfolio Transactions.”15 It explicitly included the risk aversion of traders, and introduced the idea of liquidity-adjusted value at risk as a metric for trading strategies. Okay, let’s call this Algos 201, but again, the authors do a fine job explaining this for the mathematically inclined. This work has been very widely adopted in today’s algo systems. From the abstract: We consider the execution of portfolio transactions with the aim of minimizing a combination of volatility risk and transaction costs arising from permanent and temporary market impact.

For a simple linear cost model, we explicitly construct the efficient frontier in the space of time-dependent liquidation strategies, which have minimum expected cost for a given level of uncertainty. We may then select optimal strategies either by minimizing a quadratic utility function, or minimizing Value at Risk . . . , that explicitly considers the tradeoff between volatility risk and liquidation costs. Figure 3.2 is worth many words in understanding this trading model, and the intuitively appealing implications of its results for traders of different risk tolerances. Trader A is realistically risk-averse, accelerating to reduce risk at the cost of higher impact.


pages: 192 words: 75,440

Getting a Job in Hedge Funds: An Inside Look at How Funds Hire by Adam Zoia, Aaron Finkel

backtesting, barriers to entry, Bear Stearns, collateralized debt obligation, commodity trading advisor, Credit Default Swap, credit default swaps / collateralized debt obligations, deal flow, discounted cash flows, family office, financial engineering, fixed income, global macro, high net worth, interest rate derivative, interest rate swap, Long Term Capital Management, managed futures, merger arbitrage, offshore financial centre, proprietary trading, random walk, Renaissance Technologies, risk-adjusted returns, rolodex, short selling, side project, statistical arbitrage, stock buybacks, stocks for the long run, systematic trading, two and twenty, unpaid internship, value at risk, yield curve, yield management

SAMPLE JOB SEARCHES To further illustrate what hedge funds look for when hiring various types of risk managers, we thought it would be helpful to include some job specifications from actual searches. Search 1: Hedge Fund Risk Analyst Note: This fund has a director of risk management who is looking for an additional resource (risk analyst) to join his team and develop within the firm. Description • Responsible for periodic report production, including: • Value at risk (VaR) and volatility reporting by portfolio. • Back-testing and historical performance measurement. • Portfolio segmentation analysis. • Factor analysis reporting. • Position level: • Expected return by position. • Risk analysis by position. • Marginal impact. • Relative risk/reward performance: • Stress testing.

. • Modeled financial data including scenario total returns across large asset/liability portfolios, risk management metrics and VaR, derivative hedging strategies, duration of bank deposits, portable alpha, and CORE+ portfolios. Firm Proprietary Trading – Risk Management • Project analysis of trading risk issues and P&L across the entire firm’s trading business, working under the Global Head of Market Risk and reporting to the executive committee. • Redeveloped reporting of main risk metrics (delta, gamma, vega, P&L, VaR, scenarios, etc.) to better highlight emerging risk factors, new trades, and business concerns across firm’s equity derivatives business, including market making, arbitrage, and structured and exotic derivatives. • Designed risk management procedures for growing billion-dollar hedge-fund-linked derivative structures. 2002 SMALL HEDGE FUND New York, NY Analyst – Investment Research • Performed fundamental analysis and made investment recommendations for event-driven hedge fund investigating opportunities in distressed debt, turnaround, merger, and spin-off situations. • Financial statement analysis, including FCF modeling, capital structure analysis, reviews of bond covenants, indentures, and footnotes in order to develop a valuation assessment. 1997–2001 Technology Industry Held front office positions in sales and consulting at Intel, Net Perceptions, and Euro RSCG.


pages: 782 words: 187,875

Big Debt Crises by Ray Dalio

Alan Greenspan, Asian financial crisis, asset-backed security, bank run, banking crisis, basic income, Bear Stearns, Ben Bernanke: helicopter money, break the buck, Bretton Woods, British Empire, business cycle, buy the rumour, sell the news, capital controls, central bank independence, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, currency risk, declining real wages, equity risk premium, European colonialism, fiat currency, financial engineering, financial innovation, foreign exchange controls, German hyperinflation, global macro, housing crisis, implied volatility, intangible asset, it's over 9,000, junk bonds, Kickstarter, land bank, large denomination, low interest rates, manufacturing employment, margin call, market bubble, market fundamentalism, military-industrial complex, Money creation, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, Northern Rock, Ponzi scheme, price stability, private sector deleveraging, purchasing power parity, pushing on a string, quantitative easing, refrigerator car, reserve currency, risk free rate, Savings and loan crisis, short selling, short squeeze, sovereign wealth fund, subprime mortgage crisis, too big to fail, transaction costs, universal basic income, uptick rule, value at risk, yield curve

Why Banks and Investors Were So Exposed to Risky Mortgage Securities Why were investors, banks, rating agencies and policy makers misled into thinking mortgage securities were less risky than they actually were? A key reason is the way risk is analyzed. Consider the conventional way investors think about risk. At the time, Value at Risk (VAR), which is a measure of recent volatility in markets and portfolios, was commonly used by investment firms and commercial banks to determine the likely magnitude and occurrence of losses. It typically uses recent volatility as the main input to how much risk (i.e., what size positions) one could comfortably take.

If anything, I believe that one should bet on the opposite of what happened lately, because boring years tend to sow the seeds of future instability, as well as making the next downturn worse. That’s because low volatility and benign VAR estimates encourage increased leverage. At the time, some leverage ratios were nearing 100:1. To me, leverage is a much better indicator of future volatility than VAR. In 2007, many banks and investors were heavily exposed to subprime mortgages, since the instruments had not yet had a loss cycle or experienced much volatility. VAR was also self-reinforcing on the down side, because increased market volatility at the peak of the crisis in 2008 made their statistical riskiness look even higher, causing even more selling.

This way of thinking about risk caused many investors to increase their exposures beyond what would normally be seen as prudent. They looked at the recent volatility in their VAR calculations, and by and large expected it to continue moving forward. This is human nature and it was dumb because past volatility and past correlations aren’t reliable forecasts of future risks. But it was very profitable. In fact, when we were cutting back on our positions, our clients urged us to increase them because our VAR was low. We explained why we didn’t do that. Extrapolating current conditions forward and imagining that they will be just a slightly different version of today is to us bad relative to considering the true range of possibilities going forward.


pages: 304 words: 80,965

What They Do With Your Money: How the Financial System Fails Us, and How to Fix It by Stephen Davis, Jon Lukomnik, David Pitt-Watson

activist fund / activist shareholder / activist investor, Admiral Zheng, banking crisis, Basel III, Bear Stearns, behavioural economics, Bernie Madoff, Black Swan, buy and hold, Carl Icahn, centralized clearinghouse, clean water, compensation consultant, computerized trading, corporate governance, correlation does not imply causation, credit crunch, Credit Default Swap, crowdsourcing, David Brooks, Dissolution of the Soviet Union, diversification, diversified portfolio, en.wikipedia.org, financial engineering, financial innovation, financial intermediation, fixed income, Flash crash, Glass-Steagall Act, income inequality, index fund, information asymmetry, invisible hand, John Bogle, Kenneth Arrow, Kickstarter, light touch regulation, London Whale, Long Term Capital Management, moral hazard, Myron Scholes, Northern Rock, passive investing, Paul Volcker talking about ATMs, payment for order flow, performance metric, Ponzi scheme, post-work, principal–agent problem, rent-seeking, Ronald Coase, seminal paper, shareholder value, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical model, Steve Jobs, the market place, The Wealth of Nations by Adam Smith, transaction costs, Upton Sinclair, value at risk, WikiLeaks

Somehow, our natural skepticism is dampened by sophisticated technology. Perhaps the most widespread mathematical modeling system used by the finance sector is “value at risk,” or “VaR,” as it is known in trading rooms and risk management offices. Don’t let the jargon intimidate you; value at risk is exactly what it sounds. It tries to predict, given how you are investing your money, how much value you lose, and with what probability, over any specified time period. VaR analysis might tell you, for instance, that over the next year you can be 95 percent sure that your portfolio will not lose more than 10 percent.

See also High-frequency trading Trading platforms, that protect investors, 88–89 Transaction costs, 127, 169–70, 255n4 Transparency: in governance, 97–100, 224 in People’s Pension, 203–4, 207–8 of financial institutions, 229–30 regarding fees, 53–54, 60 regulation requiring, 146 Trillium, 77 Triodos Bank, 111 TripAdvisor, 127 Trust, 256n14 finance industry and, 56, 187 financial system and, 176–77 globalization and, 186–87 in government, 141 markets and, 178, 181 Trustee boards, of pension funds, 105–6 Trustee Network, 121 Trustees, 105–6, 108–9, 137–38, 140, 205, 207, 224–25, 229 Twitter, 114, 115, 116 Uncertainty, risk and, 172, 261n35 Unequal treaties, 168–69 United Airlines, 114–15 United Kingdom: financial services as percent of economy, 16 index funds, 57 investor coalitions, 89 laws to protect shareholders, 152 switch to defined contribution plans, 197–98 trust in finance industry, 56 women on corporate boards, 247n45 United Nations Principles for Responsible Investment (UN PRI), 58, 90, 112, 117, 140, 207 UN Environment Program, Finance Initiative, 138 United States: defined benefit funds, 105 defined contribution plans, 100, 102 director elections, 79 disclosure rules, 97–98 fiduciary duty and brokers, 256n23 flash crash, 51–52 fund governance regulation, 107–8 index funds, 57 investor coalitions, 90 lack of corporate governance code, 205, 267n3 regulation in aftermath of financial crisis, 124 stock market crash (2013), 51 switch to defined contribution plans, 197–98 tax liability for executive pay, 69, 85 trust in finance industry, 56 women on corporate boards, 247n45 US Office of the Comptroller, 108 US Commodity Futures Trading Commission, 52 US Department of Labor, 60, 107, 139–40 University of Oxford, 190 University students, governance reform and, 121–22 Urwin, Roger, 206 Value at risk (VaR), 39–40 Values, economies and, 178 Van Clieaf, Mark, 68 van der Vondel, Joost, 262n49 Vanguard, 6, 139, 235n24 Volcker, Paul, 267n1 von Hayek, Friedrich, 165 Voting: derivatives and, 80, 82–83, 93 disclosure of, 93, 108, 120, 121, 140, 207 for directors, 78–79 fund managers and, 75–77 individual investors and, 91, 120, 223 Vulture investors, 248n50 Wall Street Journal (newspaper), 29, 52 Wallace, Robert, 14, 199–202, 209 Walras, Leon, 159–60 Walter, Elisse, 88 Warren, Elizabeth, 129, 130 Washington, George, 157 The Wealth of Nations (Smith), 158, 161 Webb, David, 115 Weber, Max, 167 Webster, Alexander, 14, 199–202, 209 Weisman, Andrew, 37, 38 White, William, 259n5 Williamson, Michael, 59 Williamson, Oliver, 262n52 Women, on corporate boards, 247n45 Wong, Simon, 104 World Bank, 73, 151 World Economic Forum, 103–4 World Federation of Exchanges, 64–65 WorldCom, 44 www.corpgov.net, 116–17 Yahoo!

See Liability-driven investing (LDI) Legislation on corporate governance, 9–10 Lehman Brothers, 25 Lemming standard, 137–38 Lenders, financial system as intermediator between borrowers and, 17, 19–22, 47, 74, 211–15 Leverage ratio, 215 Levitt, Arthur, 28 Liability-driven investing (LDI), 54–56, 264n2 Liar loans, 47 Libor, 257n32 Limited liability company, 21, 184–85, 237n7 Lincoln, Abraham, 157 LinkedIn, 116 Lipsey, Richard, 236n34 Lipton, Martin, 8 Liquidity, 17 Liquidity crisis, 74 Llewellyn, Karl, 262n52 Loans, classification of risk, 129, 175, 212–13 Long-Term Capital Management, 38, 164, 260n20 Long-term growth, emphasizing, 223 Long-term investment, 148: evergreen direct investing, 87 tax policy encouraging, 92, 223–24 Longevity risk, 194–95 Loopholes, regulation and exploitation of, 130 Lorsch, Jay, 8 MacDonald, Tim, 59, 87 Macey, Jon, 249n3 Macroeconomics, 179 Madoff, Bernard, 105 Mann, Harinder, 263n1, 266n28 Mao Tse Tung, 157 Marginal cost, 160 Marginal value, 160 Market capitalization, 45–46 Market discipline, 152–53, 258n46 Market economies, 142, 177, 181 regulation of, 125–28 Market institutions, 170 Markets: creating fiduciary behavior and, 152–53 regulation and, 125–28, 134 transaction costs and, 169–70 trust and, 178, 181 Marshall, Alfred, 177 Marx, Karl, 159 Mathematical modeling: atomized regulation and, 130 liability-driven investing and, 55–56 narrowness of, 153 overreliance on theoretical, 168–71 publishing economic research and, 189–90 recalibration of, 264n9 value at risk, 39–40 weakness of, 40–44. See also Economic modeling Max Planck Institute for Research on Collective Goods, 215 McRitchie, James, 116–17 Mercer, 122 Merrill Lynch, 44 Merton, Robert, 260n20 MFS Technologies, 82 Microeconomics, 179, 181–82 Millstein Center for Corporate Governance and Performance, 265n13 Millstein, Ira, 8 Minow, Nell, 207 Mirvis, Theodore, 8 Misalignment indicators, 104 Molinari, Claire, 112 Money managers, using collective action to allow focus on benefits for all, 89–90 “The Monkey Business Illusion” (video), 174 Monks, Bob, 62 Moral hazard, 73 Morality: economics and, 158 trade, 177 Morningstar, 34, 35, 36–37, 101, 122, 208, 225 Mortgages: chain of agents involved in, 31–32 subprime, 38, 40, 47 Murninghan, Marcy, 122 Mutual funds: agency capitalism and, 77–78 boards of, 205–6, 265n14 chain of agents in, 31 disclosure rules, 97 duration of holdings, 243n4, 258n41 failure to protect investors’ interests, 6–7 governance and performance of, 101–4, 224–25 grassroots campaigns influencing, 117 self-evaluation of, 110 votes on shareholder resolutions, 102 Mylan Laboratories, 81 Myopia, 66, 68 National Employment Savings Trust (NEST), 111, 206, 208 National governance code, 205, 265n13 Navalny, Alexei, 115–16 NEST.


pages: 237 words: 50,758

Obliquity: Why Our Goals Are Best Achieved Indirectly by John Kay

Andrew Wiles, Asian financial crisis, Bear Stearns, behavioural economics, Berlin Wall, Boeing 747, bonus culture, British Empire, business process, Cass Sunstein, computer age, corporate raider, credit crunch, Daniel Kahneman / Amos Tversky, discounted cash flows, discovery of penicillin, diversification, Donald Trump, Fall of the Berlin Wall, financial innovation, Goodhart's law, Gordon Gekko, greed is good, invention of the telephone, invisible hand, Jane Jacobs, junk bonds, lateral thinking, Long Term Capital Management, long term incentive plan, Louis Pasteur, market fundamentalism, Myron Scholes, Nash equilibrium, pattern recognition, Paul Samuelson, purchasing power parity, RAND corporation, regulatory arbitrage, shareholder value, Simon Singh, Steve Jobs, Suez canal 1869, tacit knowledge, Thales of Miletus, The Death and Life of Great American Cities, The Predators' Ball, The Wealth of Nations by Adam Smith, ultimatum game, urban planning, value at risk

We, and they, learned that they were wrong. The most widely used template in the banking industry was called “value at risk” (VAR) and elaborated by JPMorgan. The bank published the details and subsequently spun off a business, RiskMetrics, which promotes it still.2 These risk models are based on analysis of the volatility of individual assets or asset classes and—crucially—on correlations, the relationships among the behaviors of different assets. The standard assumptions of most value-at-risk models are that the dispersion of investment returns follows the normal distribution, the bell curve that characterizes so many natural and social phenomena, and that future correlations will reproduce past ones.

root method Rotella, Bob Rousseau, Jean-Jacques rules Saint-Gobain salesmen Salomon Brothers Samuelson, Paul Santa Maria del Fiore cathedral Scholes, Myron science scorecard Scottish Enlightenment Sculley, John Sears securities selfish gene September 11 attacks (2001) shareholder value share options Sieff, Israel Sierra Leone Simon, Herbert simplification Singapore Singer Smith, Adam Smith, Ed Smith, Will SmithKline soccer (English football) social contract socialism social issues socialist realism sociopaths Solon Sony Sony Walkman Soros, George Soviet Union sports Stalin, Joseph “Still Muddling, Not Yet Through” (Lindblom) Stockdale, James Stockdale Paradox stock prices Stone, Oliver successive limited comparison sudoku Sugar, Alan Sunbeam Sunstein, Cass Super Cub motorcycles superstition surgery survival sustainability Taleb, Nassim Nicholas Tankel, Stanley target goals teaching quality assessment technology see also computers teleological fallacy telephones Tellus tennis Tetlock, Philip Tet Offensive (1968) Thales of Miletus Thornton, Charles Bates “Tex” tic-tac-toe Tolstoy, Leo transnational corporations transportation Travelers Treasury, U.S. trials Trump, Donald TRW 2001: A Space Odyssey Typhoon (Conrad) ultimatum games uncertainty United Nations United States Unités d’Habitation unplanned evolution urban planning value at risk (VAR) van Gogh, Vincent van Meegeren, Han Vasari, Giorgio Vermeer, Johannes Victorian era Vietnam War Vioxx volatility Wall Street Walton, Sam Wason, Peter Wason test wealth Wealth of Nations, The (Smith) Weill, Sandy Weir, Peter Welch, Jack Whately, Archbishop What Is to Be Done?

., Perspectives on Strategy from the Boston Consulting Group (New York: John Wiley, 1998). 5 For similar illusions, see http://www.planetperplex.com/en/item131. Chapter 12: Abstraction—Why Models Are Imperfect Descriptions of Reality 1 Jorge Luis Borges, A Universal History of Infamy (Harmondsworth, UK: Penguin, 1975), p. 131. 2 Value at risk is a group of related models that compute the maximum potential change in value of a portfolio of assets under “normal” market conditions. (See also: JPMorgan and Reuters, RiskMetrics—Technical Document, 4th ed. (New York: Morgan Guaranty Trust Company of New York, 1996); Joe Nocera, “Risk Management,” New York Times, January 4, 2009.) 3 Bruce Pandolfini, Kasparov and Deep Blue: The Historic Chess Match Between Man and Machine (New York: Simon & Schuster, 1997). 4 David G.


Digital Accounting: The Effects of the Internet and Erp on Accounting by Ashutosh Deshmukh

accounting loophole / creative accounting, AltaVista, book value, business continuity plan, business intelligence, business logic, business process, call centre, computer age, conceptual framework, corporate governance, currency risk, data acquisition, disinformation, dumpster diving, fixed income, hypertext link, information security, interest rate swap, inventory management, iterative process, late fees, machine readable, money market fund, new economy, New Journalism, optical character recognition, packet switching, performance metric, profit maximization, semantic web, shareholder value, six sigma, statistical model, supply chain finance, supply-chain management, supply-chain management software, telemarketer, transaction costs, value at risk, vertical integration, warehouse automation, web application, Y2K

Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 302 Deshmukh • Supporting portfolio hierarchies • Handling portfolio audits Market risk analyzer manages risks associated with the stock market, foreign currency holdings and fluctuations in interest rates. Stock market positions such as mark-tomarket valuations can be evaluated using the built-in tools. Different calculations, such as risk and return, exposure, future values and value at risk, can be calculated using this analyzer. Accounting standards such as FAS 133 are supported. Simulation tools can be used to run valuation scenarios based on actual and simulated market prices. These tools can also be used to simulate changes in interest and currency exchange rates and run hypothetical valuation scenarios.

Accounting software that serves the mid-size market can provide a full financial suite and advanced industry-specific modules, and offer e-commerce solutions. This software can support multiple users, operate on multiple operating systems and come with an embedded database or work with any existing relational database products. The software is expensive and is generally sold by Value Added Resellers (VARs). VARs specialize in a particular software package and serve as consultants during installation and operation of the system. The ERP packages at the high end are extremely expensive — a software cost of millions of dollars being merely a drop in the bucket compared to extensive consulting, training, reengineering of workflows and restructuring of organizations costs — and require armies of consultants and multiple years to install and make operational.

Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 394 Index USA PATRIOT (Uniting and Strengthening America by Providing Appropriate Tools Required to Intercept and Obstruct Terrorism) 332 V VAB (value added bank) 109 value added bank (VAB) 109 value added networks (VANs) 97 value added resellers (VARs) 27 VANs (value added networks) 97 VARs (value added resellers) 27 vendor managed inventory (VMI) 237 VeriSign 348 vertical accounting 28 virtual cards 173 virtual close 262 virtual memory system (VMS) 24 virtual private network (VPN) 217, 353 visibility 234 VMI (vendor managed inventory) 237 VMS (virtual memory system) 24 VPN (virtual private network) 217, 353 vulnerability scanning tools 358 W W3C 47 WAIS (wide area information services) 6 WANs (wide area networks) 1, 22 warehouse management systems (WMSs) 10, 132, 150 warehousing 149 Web based distributed authoring and versioning (WebDAV) 285 Web content filtering 352 Web data transfer 116 WebDAV (Web based distributed authoring and versioning) 285 Web money 183 Web portals 8 Web Services Description Language (WSDL) 140 Web storefronts 38 WebTrust 372 well-formed XML document 50 what-if analysis 255 wide area information services (WAIS) 6 wide area networks (WANs) 1, 22 Windows 20 Windows New Technology (NT) 24 Windows NT (New Technology) 24 wireless networks 337 WMSs (warehouse management systems) 10, 132, 150 World Wide Web (WWW) 6 worms 327 WSDL (Web Services Description Language) 140 X XBRL (eXtensible Business Reporting Language) 13, 43, 63, 64, 72, 77, 78 XBRL DOM 78 XBRL instance documents 72 XBRL international 63 XBRL repositories 77 XBRL specifications 63 XBRL taxonomies 64 XBRL tools 77 XBRL.org 63 XFRML (eXtensible Financial Reporting Modeling Language) 63 XML (eXtensible Markup Language) 42, 43, 48 XML, canonical 58 XML, commerce (cXML) 87 XML data query language (XQuery) 58 XML document 48 XML document, well-formed 50 XML, eCX XML (Electronic Catalog XML) 86 XML, free-form 50 XML namespaces 55 XML schema 51 XML Spy 56 XML syntax 49 XQuery (XML Data Query Language) 58 xRM 33 XSL (eXtensible Stylesheet Language) 57 XSL transformation (XSLT) 58 XSLT (XSL transformation) 58 Copyright © 2006, Idea Group Inc.


pages: 198 words: 53,264

Big Mistakes: The Best Investors and Their Worst Investments by Michael Batnick

activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, AOL-Time Warner, asset allocation, Bear Stearns, behavioural economics, bitcoin, Bretton Woods, buy and hold, buy low sell high, Carl Icahn, cognitive bias, cognitive dissonance, Credit Default Swap, cryptocurrency, Daniel Kahneman / Amos Tversky, endowment effect, financial engineering, financial innovation, fixed income, global macro, hindsight bias, index fund, initial coin offering, invention of the wheel, Isaac Newton, Jim Simons, John Bogle, John Meriwether, Kickstarter, Long Term Capital Management, loss aversion, low interest rates, Market Wizards by Jack D. Schwager, mega-rich, merger arbitrage, multilevel marketing, Myron Scholes, Paul Samuelson, Pershing Square Capital Management, quantitative easing, Reminiscences of a Stock Operator, Renaissance Technologies, Richard Thaler, Robert Shiller, short squeeze, Snapchat, Stephen Hawking, Steve Jobs, Steve Wozniak, stocks for the long run, subprime mortgage crisis, transcontinental railway, two and twenty, value at risk, Vanguard fund, Y Combinator

And then the wheels fell off for Meriwether and his colleagues. All the brains in the world couldn't save them from what was coming. LTCM took financial science to its extreme – to the outer limits of sanity. They coldly calculated the odds of every wiggle for every position in their portfolio. In August 1998, they calculated that their daily VAR, or value at risk (how much they could lose), was $35 million. August 21, 1998, is the day when their faith should have evaporated, along with the $550 million that they lost.19 It was the beginning of the end. By the end of the month, they had lost $1.9 billion, putting the fund down 52% year‐to‐date. The death spiral was in full effect.

., 48 Time horizons, 120 Time Warner, AOL merger, 49 Tim Ferriss Show, The, (podcast), 150 Tim Hortons, spinoff, 89 Tract on Monetary Reform, A, (Keynes), 125–126 Trader (Jones), 119 Trustees Equity Fund, decline, 50 Tsai, Jerry, 65, 68 stocks, trading, 69 ten good games, 71 Tsai Management Research, sale, 70 Tversky, Amos, 81 Twain, Mark (Samuel Clemens), 25, 27, 75 bankruptcy filings, 32 money, losses, 27–32 public opinion, hypersensitivity, 31 Twilio, Sacca investment, 149 Twitter, Sacca investment, 149–150 Uber, Sacca investment, 149 Undervalued issues, selection, 10 Union Pacific, shares (sale), 18 United Copper, cornering, 19 United States housing bubble, 132 University Computing, trading level, 70 US bonds international bonds, spreads, 41 value, decline, 61 U.S. housing bubble, impact, 132 U.S. Steel, shares orders, 17 US stock portfolio, diversification, 109 U.S. stocks, intra‐year decline, 147 Valeant Pharmaceuticals, 113 Ackman targeting, 90 performance, S&P500 comparison, 113 shares, decline, 114 Valuation metrics, 160 Value at risk (VAR), 41–42 Value investing, function, 10 Value investors, problems, 58 Vanguard 500 Index returns, 52 size, 47 Vanguard Group, Inc., 51 VeriSign, Druckenmiller purchase, 104 Vranos, 133 Washington Post stocks, problems, 58 Wayne, Ronald, 148 Webster & Company bankruptcy, 31 problems, 30 Webster, Samuel Charles, 29 Wellington Fund, 48 merger, 49 operation, at‐cost basis, 51 Wellington Management, Bogle firing, 51 Wendy's, stock appreciation, 89 Wesco Financial, purchase, 142 Wheeler, Munger & Company, 141 Whiz Kids Take Over, The, 49 “Who Wants to Be a Millionaire” (Ackman), 90–91 Winning the Loser's Game (Ellis), 99 Woodman, Nick, 150 WordPress, 149 World War I, global monetary system, 122 Wozniak, Steve, 148 Wright Aeronautical, business demonstration, 3 Xerox, trading level, 70 Yahoo!


pages: 1,088 words: 228,743

Expected Returns: An Investor's Guide to Harvesting Market Rewards by Antti Ilmanen

Alan Greenspan, Andrei Shleifer, asset allocation, asset-backed security, availability heuristic, backtesting, balance sheet recession, bank run, banking crisis, barriers to entry, behavioural economics, Bernie Madoff, Black Swan, Bob Litterman, bond market vigilante , book value, Bretton Woods, business cycle, buy and hold, buy low sell high, capital asset pricing model, capital controls, carbon credits, Carmen Reinhart, central bank independence, classic study, collateralized debt obligation, commoditize, commodity trading advisor, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, deal flow, debt deflation, deglobalization, delta neutral, demand response, discounted cash flows, disintermediation, diversification, diversified portfolio, dividend-yielding stocks, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, fiat currency, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, framing effect, frictionless, frictionless market, G4S, George Akerlof, global macro, global reserve currency, Google Earth, high net worth, hindsight bias, Hyman Minsky, implied volatility, income inequality, incomplete markets, index fund, inflation targeting, information asymmetry, interest rate swap, inverted yield curve, invisible hand, John Bogle, junk bonds, Kenneth Rogoff, laissez-faire capitalism, law of one price, London Interbank Offered Rate, Long Term Capital Management, loss aversion, low interest rates, managed futures, margin call, market bubble, market clearing, market friction, market fundamentalism, market microstructure, mental accounting, merger arbitrage, mittelstand, moral hazard, Myron Scholes, negative equity, New Journalism, oil shock, p-value, passive investing, Paul Samuelson, pension time bomb, performance metric, Phillips curve, Ponzi scheme, prediction markets, price anchoring, price stability, principal–agent problem, private sector deleveraging, proprietary trading, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, reserve currency, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Robert Shiller, savings glut, search costs, selection bias, seminal paper, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stochastic volatility, stock buybacks, stocks for the long run, survivorship bias, systematic trading, tail risk, The Great Moderation, The Myth of the Rational Market, too big to fail, transaction costs, tulip mania, value at risk, volatility arbitrage, volatility smile, working-age population, Y2K, yield curve, zero-coupon bond, zero-sum game

Antti Ilmanen Bad Homburg, November 2010 Abbreviations and acronyms AM Arithmetic Mean ATM At The Money (option) AUM Assets Under Management BEI Break-Even Inflation BF Behavioral Finance B/P Book/Price, book-to-market ratio BRP Bond Risk Premium, term premium B-S Black–Scholes C-P BRP Cochrane–Piazzesi Bond Risk Premium CAPM Capital Asset Pricing Model CAY Consumption wealth ratio CB Central Bank CCW Covered Call Writing CDO Collateralized Debt Obligation CDS Credit Default Swap CF Cash Flow CFNAI Chicago Fed National Activity Index CFO Chief Financial Officer CMD Commodity (futures) CPIyoy Consumer Price Inflation year on year CRB Commodity Research Bureau CRP Credit Risk Premium (over Treasury bond) CRRA Constant Relative Risk Aversion CTA Commodity Trading Advisor DDM Dividend Discount Model DJ CS Dow Jones Credit Suisse DMS Dimson–Marsh–Staunton D/P Dividend/Price (ratio), dividend yield DR Diversification Return E( ) Expected (conditional expectation) EMH Efficient Markets Hypothesis E/P Earnings/Price ratio, earnings yield EPS Earnings Per Share ERP Equity Risk Premium ERPB Equity Risk Premium over Bond (Treasury) ERPC Equity Risk Premium over Cash (Treasury bill) F Forward price or futures price FF Fama–French FI Fixed Income FoF Fund of Funds FX Foreign eXchange G Growth rate GARCH Generalized AutoRegressive Conditional Heteroskedasticity GC General Collateral repo rate (money market interest rate) GDP Gross Domestic Product GM Geometric Mean, also compound annual return GP General Partner GSCI Goldman Sachs Commodity Index H Holding-period return HF Hedge Fund HFR Hedge Fund Research HML High Minus Low, a value measure, also VMG HNWI High Net Worth Individual HPA House Price Appreciation (rate) HY High Yield, speculative-rated debt IG Investment Grade (rated debt) ILLIQ Measure of a stock’s illiquidity: average absolute daily return over a month divided by dollar volume IPO Initial Public Offering IR Information Ratio IRP Inflation Risk Premium ISM Business confidence index ITM In The Money (option) JGB Japanese Government Bond K-W BRP Kim–Wright Bond Risk Premium LIBOR London InterBank Offered Rate, a popular bank deposit rate LP Limited Partner LSV Lakonishok–Shleifer–Vishny LtA Limits to Arbitrage LTCM Long-Term Capital Management MA Moving Average MBS (fixed rate, residential) Mortgage-Backed Securities MIT-CRE MIT Center for Real Estate MOM Equity MOMentum proxy MSCI Morgan Stanley Capital International MU Marginal Utility NBER National Bureau of Economic Research NCREIF National Council of Real Estate Investment Fiduciaries OAS Option-Adjusted (credit) Spread OTM Out of The Money (option) P Price P/B Price/Book (valuation ratio) P/E Price/Earnings (valuation ratio) PE Private Equity PEH Pure Expectations Hypothesis PT Prospect Theory r Excess return R Real (rate) RE Real Estate REITs Real Estate Investment Trusts RWH Random Walk Hypothesis S Spot price, spot rate SBRP Survey-based Bond Risk Premium SDF Stochastic Discount Factor SMB Small Minus Big, size premium proxy SR Sharpe Ratio SWF Sovereign Wealth Fund TED Treasury–Eurodollar (deposit) rate spread in money markets TIPS Treasury Inflation-Protected Securities, real bonds UIP Uncovered Interest Parity (hypothesis) VaR Value at Risk VC Venture Capital VIX A popular measure of the implied volatility of S&P 500 index options VMG Value Minus Growth, equity value premium proxy WDRA Wealth-Dependent Risk Aversion X Cash flow Y Yield YC Yield Curve (steepness), term spread YTM Yield To Maturity YTW Yield To Worst Disclaimer Antti Ilmanen is a Senior Portfolio Manager at Brevan Howard, one of Europe’s largest hedge fund managers.

Principal–agent problems shorten horizons from both sides, a phenomenon that can make the long-horizon investor lose his natural edge. Market turmoil in 1998 and 2007–2008 taught us additional features that should discourage arbitrage activities—VaR-based risk management and crowded trade risk:• Risk management systems that make sense for any one investor can increase systemic risk. A vicious circle can arise when rising risks (in VaR models or in other reactive risk measures) trigger mandatory or voluntary position reductions (but the problem is much worse if mandatory); widespread liquidations can destabilize the markets and require further reductions.

They blamed widespread belief in the EMH, and more loosely “market fundamentalism”, for the laissez faire attitude of policymakers and regulators: letting leverage and asset booms grow unchecked and allowing ever more complex financial instruments and questionable sales practices flourish without restraint, under the cover that the market would inevitably get the prices of these securities right. Other criticisms come as though out of a scattergun: some attack the classical notion that competitive markets are inherently self-stabilizing, others blame the Fed’s asymmetric policy responses, while others fault the use of normal distribution and VaR-based risk management in a world where fat tails dominate. All of these criticisms are only tangentially related to the EMH, yet some of the critics do not seem to realize that. To me, a fair conclusion is that recent events have undermined the validity of the EMH’s main idea—that market prices are always “right” (near the fair value)—but have underlined the validity of its main implication for most investors—that beating the markets is extremely difficult (no free lunches).


pages: 430 words: 109,064

13 Bankers: The Wall Street Takeover and the Next Financial Meltdown by Simon Johnson, James Kwak

Alan Greenspan, American ideology, Andrei Shleifer, Asian financial crisis, asset-backed security, bank run, banking crisis, Bear Stearns, Bernie Madoff, Black Monday: stock market crash in 1987, Bonfire of the Vanities, bonus culture, book value, break the buck, business cycle, business logic, buy and hold, capital controls, Carmen Reinhart, central bank independence, Charles Lindbergh, collapse of Lehman Brothers, collateralized debt obligation, commoditize, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, currency risk, Edward Glaeser, Eugene Fama: efficient market hypothesis, financial deregulation, financial engineering, financial innovation, financial intermediation, financial repression, fixed income, George Akerlof, Glass-Steagall Act, Gordon Gekko, greed is good, Greenspan put, Home mortgage interest deduction, Hyman Minsky, income per capita, information asymmetry, interest rate derivative, interest rate swap, junk bonds, Kenneth Rogoff, laissez-faire capitalism, late fees, light touch regulation, Long Term Capital Management, low interest rates, market bubble, market fundamentalism, Martin Wolf, Michael Milken, money market fund, moral hazard, mortgage tax deduction, Myron Scholes, Paul Samuelson, Ponzi scheme, price stability, profit maximization, proprietary trading, race to the bottom, regulatory arbitrage, rent-seeking, Robert Bork, Robert Shiller, Ronald Reagan, Saturday Night Live, Satyajit Das, Savings and loan crisis, sovereign wealth fund, Tax Reform Act of 1986, The Myth of the Rational Market, too big to fail, transaction costs, Tyler Cowen, value at risk, yield curve

Quoted in Jenny Anderson, “Despite Bailouts, Business as Usual at Goldman,” The New York Times, August 5, 2009, available at http://www.nytimes.com/2009/08/06/business/06goldman.html. 71. Felix Salmon, “Chart of the Day: Goldman VaR,” Reuters, July 15, 2009, available at http://blogs.reuters.com/felix-salmon/2009/07/15/chart-of-the-day-goldman-var/. See also Andrew Ross Sorkin, “Taking a Chance on Risk, Again,” DealBook Blog, The New York Times, September 17, 2009, available at http://dealbook.blogs.nytimes.com/2009/09/17/taking-a-chance-on-risk-again/. While VaRvalue-at-risk—is a poor way of estimating potential losses under extreme market conditions, it does measure the change in the riskiness of a portfolio relative to historical data. 72.

JPMorgan Chase, Goldman Sachs, and Morgan Stanley alone accounted for 42 percent of the market for equity underwriting in the first half of 2009.69 Finally, Goldman was making money the oldfashioned way—by taking on more risk. As the bank’s president, Gary Cohn, said in August 2009, “Our risk appetite continues to grow year on year, quarter on quarter, as our balance sheet and liquidity continue to grow.”70 And Goldman’s value-at-risk—a quantitative measure of the amount it stood to lose on a given day—after dipping slightly in summer 2008, continued to climb throughout the crisis to levels in 2009 five times as high as in 2002.71 However, the clearest indication that Wall Street was back to business as usual was the amount of money earmarked for bonuses.


The Global Money Markets by Frank J. Fabozzi, Steven V. Mann, Moorad Choudhry

asset allocation, asset-backed security, bank run, Bear Stearns, Bretton Woods, buy and hold, collateralized debt obligation, credit crunch, currency risk, discounted cash flows, discrete time, disintermediation, Dutch auction, financial engineering, fixed income, Glass-Steagall Act, high net worth, intangible asset, interest rate derivative, interest rate swap, land bank, large denomination, locking in a profit, London Interbank Offered Rate, Long Term Capital Management, margin call, market fundamentalism, money market fund, moral hazard, mortgage debt, paper trading, Right to Buy, short selling, stocks for the long run, time value of money, value at risk, Y2K, yield curve, zero-coupon bond, zero-sum game

These have included enhancing the role of the head of Treasury and the asset and liability committee (ALCO), using other risk exposure measures such as option-adjusted spread and value-at-risk (VaR), and integrating the traditional interest-rate risk management with credit risk and operational risk. The increasing use of credit derivatives has facilitated this integrated approach to risk management. The additional roles of the ALM desk may include: ■ using the VaR tool to assess risk exposure; ■ integrating market risk and credit risk; ■ using new risk-adjusted measures of return; Asset and Liability Management 285 ■ optimizing portfolio return; ■ proactively managing the balance sheet; this includes giving direction on securitization of assets (removing them from the balance sheet), hedging credit exposure using credit derivatives, and actively enhancing returns from the liquidity book, such as entering into security lending and repo.

Treasury coupon securities, 155 U.S. Treasury dealers, 33–35 U.S. Treasury notes, 16, 31, 119– 121 10-year, 133 U.S. Treasury rates, 98 U.S. Treasury securities, 24. See also Maturity supply, decrease, 265 U.S. Treasury yield curve. See On-the-run U.S. Treasury yield curve USSPS Index GP, 254 Valuation model, 117 Value-at-risk (VaR), 284, 302. See also Credit limits, 282 Vanilla swap, 261 Variable-rate closed-end HELs, 198 Variable-rate gap, 293 Variable-rate SBA loans, 207 Variable-rate securities, 102 Variation margin, 211 Vaughan, Mark D., 71 Veterans Administration (VA), 155, 202 Visa, 192, 193 Volatility characteristics.

If the present value of the swap at the time of default is net positive, then a bank is at risk of loss of this amount. While market risk can be hedged, it is more problematic to hedge credit risk. The common measures taken include limits on lending lines, collateral, and diversification across counterparty sectors, as well as a form of credit value-at-risk to monitor credit exposures. A bank therefore is at risk of loss due to counterparty default for all its swap transactions. If at the time of default, the net present value of the swap is positive, this amount is potentially at risk and will probably be written off. If the value of the swap is negative at the time of default, in Swaps and Caps/Floors 263 theory this amount is a potential gain to the bank, although in practice the counterparty’s administrators will try to recover the value for their client.


Principles of Corporate Finance by Richard A. Brealey, Stewart C. Myers, Franklin Allen

3Com Palm IPO, accelerated depreciation, accounting loophole / creative accounting, Airbus A320, Alan Greenspan, AOL-Time Warner, Asian financial crisis, asset allocation, asset-backed security, banking crisis, Bear Stearns, Bernie Madoff, big-box store, Black Monday: stock market crash in 1987, Black-Scholes formula, Boeing 747, book value, break the buck, Brownian motion, business cycle, buy and hold, buy low sell high, California energy crisis, capital asset pricing model, capital controls, Carl Icahn, Carmen Reinhart, carried interest, collateralized debt obligation, compound rate of return, computerized trading, conceptual framework, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, cross-border payments, cross-subsidies, currency risk, discounted cash flows, disintermediation, diversified portfolio, Dutch auction, equity premium, equity risk premium, eurozone crisis, fear index, financial engineering, financial innovation, financial intermediation, fixed income, frictionless, fudge factor, German hyperinflation, implied volatility, index fund, information asymmetry, intangible asset, interest rate swap, inventory management, Iridium satellite, James Webb Space Telescope, junk bonds, Kenneth Rogoff, Larry Ellison, law of one price, linear programming, Livingstone, I presume, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Bachelier, low interest rates, market bubble, market friction, money market fund, moral hazard, Myron Scholes, new economy, Nick Leeson, Northern Rock, offshore financial centre, PalmPilot, Ponzi scheme, prediction markets, price discrimination, principal–agent problem, profit maximization, purchasing power parity, QR code, quantitative trading / quantitative finance, random walk, Real Time Gross Settlement, risk free rate, risk tolerance, risk/return, Robert Shiller, Scaled Composites, shareholder value, Sharpe ratio, short selling, short squeeze, Silicon Valley, Skype, SpaceShipOne, Steve Jobs, subprime mortgage crisis, sunk-cost fallacy, systematic bias, Tax Reform Act of 1986, The Nature of the Firm, the payments system, the rule of 72, time value of money, too big to fail, transaction costs, University of East Anglia, urban renewal, VA Linux, value at risk, Vanguard fund, vertical integration, yield curve, zero-coupon bond, zero-sum game, Zipcar

Bankers refer to this as the value at risk (or VAR) on the Starbucks bonds. There are a number of ways to improve this back-of-the-envelope estimate of the value at risk. For example, we assumed that the yield spreads on corporate bonds are constant. But, if investors become more reluctant to take on credit risk, you could lose much more than 7% on your investment. Notice also that when we calculated the risk from investing in Starbucks debt, we looked only at how the price of the bonds would be affected by a change in credit rating. If we wanted a comprehensive measure of value at risk, we would need to recognize that risk-free interest rates, too, may change over the year.

Unseasoned issue Issue of a security for which there is no existing market (cf. seasoned issue). Unsystematic risk Specific risk. V Value additivity Rule that the value of the whole must equal the sum of the values of the parts. Value at risk (VAR) The probability of portfolio losses exceeding some specified proportion. Value stock A stock that is expected to provide steady income but relatively low growth (often refers to stocks with a low ratio of market-to-book value). Vanilla issue Issue without unusual features. VAR Value at risk. Variable-rate demand bond (VRDB) Floating-rate bond that can be sold back periodically to the issuer. Variance Mean squared deviation from the expected value; a measure of variability.

., 573n Cram-down, 852 Credit analysis, 783 Credit cards, 788 Credit decision, 783–786 Credit default swaps, 588–589 Credit insurance, 786 Credit management, 781–787 collection policy in, 786–787 credit analysis in, 783 credit decision in, 783–786 promise to pay and, 782 terms of sale in, 781–782 CreditMetrics, 601, 601n Credit risk, 585–603 bank loan, 626 bond ratings, 65–66, 67, 595–597, 601–602, 609, 623, 628n option to default, 590–602 value at risk (VAR), 601–602 yields on corporate debt, 585–589 Credit scoring, 597–599 Credit Suisse, 379, 623 Credit transfer, 788 Cross-border leasing, 652 C&S Sovran, 812 CSX, 224 Cum dividend (with dividend), 402 Cumulative capital requirements, 748–751 Cumulative preferred stock, 356 Cumulative voting, 353 Currency futures market, 695 Currency risk.


pages: 471 words: 124,585

The Ascent of Money: A Financial History of the World by Niall Ferguson

Admiral Zheng, Alan Greenspan, An Inconvenient Truth, Andrei Shleifer, Asian financial crisis, asset allocation, asset-backed security, Atahualpa, bank run, banking crisis, banks create money, Bear Stearns, Black Monday: stock market crash in 1987, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, BRICs, British Empire, business cycle, capital asset pricing model, capital controls, Carmen Reinhart, Cass Sunstein, central bank independence, classic study, collateralized debt obligation, colonial exploitation, commoditize, Corn Laws, corporate governance, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, Daniel Kahneman / Amos Tversky, deglobalization, diversification, diversified portfolio, double entry bookkeeping, Edmond Halley, Edward Glaeser, Edward Lloyd's coffeehouse, equity risk premium, financial engineering, financial innovation, financial intermediation, fixed income, floating exchange rates, Fractional reserve banking, Francisco Pizarro, full employment, Future Shock, German hyperinflation, Greenspan put, Herman Kahn, Hernando de Soto, high net worth, hindsight bias, Home mortgage interest deduction, Hyman Minsky, income inequality, information asymmetry, interest rate swap, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, iterative process, James Carville said: "I would like to be reincarnated as the bond market. You can intimidate everybody.", John Meriwether, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, junk bonds, Kenneth Arrow, Kenneth Rogoff, knowledge economy, labour mobility, Landlord’s Game, liberal capitalism, London Interbank Offered Rate, Long Term Capital Management, low interest rates, market bubble, market fundamentalism, means of production, Mikhail Gorbachev, Modern Monetary Theory, Money creation, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, mortgage tax deduction, Myron Scholes, Naomi Klein, National Debt Clock, negative equity, Nelson Mandela, Nick Bostrom, Nick Leeson, Northern Rock, Parag Khanna, pension reform, price anchoring, price stability, principal–agent problem, probability theory / Blaise Pascal / Pierre de Fermat, profit motive, quantitative hedge fund, RAND corporation, random walk, rent control, rent-seeking, reserve currency, Richard Thaler, risk free rate, Robert Shiller, rolling blackouts, Ronald Reagan, Savings and loan crisis, savings glut, seigniorage, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, spice trade, stocks for the long run, structural adjustment programs, subprime mortgage crisis, tail risk, technology bubble, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus, Thorstein Veblen, tontine, too big to fail, transaction costs, two and twenty, undersea cable, value at risk, W. E. B. Du Bois, Washington Consensus, Yom Kippur War

Meriwether echoed this view: ‘The nature of the world had changed, and we hadn’t recognized it.’98 In particular, because many other firms had begun trying to copy Long-Term’s strategies, when things went wrong it was not just the Long-Term portfolio that was hit; it was as if an entire super-portfolio was haemorrhaging.99 There was a herd-like stampede for the exits, with senior managers at the big banks insisting that positions be closed down at any price. Everything suddenly went down at once. As one leading London hedge fund manager later put it to Meriwether: ‘John, you were the correlation.’ There was, however, another reason why LTCM failed. The firm’s value at risk (VaR) models had implied that the loss Long-Term suffered in August was so unlikely that it ought never to have happened in the entire life of the universe. But that was because the models were working with just five years’ worth of data. If the models had gone back even eleven years, they would have captured the 1987 stock market crash.

United Kingdom see Britain United Netherlands East India Company see VOC United Provinces: bond market 75 currencies in 48 and East India Company see VOC and Mississippi Bubble 153-5 power of 3 rentiers in 75 rivalry between provinces 129 see also Netherlands, The United States of America: ageing population 219-21 ‘American empire’ 309-10 banking system 57-8 British investment in 293 budget deficit 118 currency policy 338 debt and bankruptcy in 59-61 defence industry 317 depression see depressions divisions in society see race divisions France and see Louisiana government bonds 323 health care and insurance 61 home ownership see property/ real estate and IMF and World Bank 309-12 immigration and population 286 imports 10 incomes 1-2 industrialization 285 inflation 108 insurance 199 international borrowing 334-5 overseas aid and investment 305-7 public ignorance about finance 10-12 real estate see property/real estate recession prospects 8 savings 333-5 social divisions see race divisions stock market 6 as subprime superpower 282 use of economic hit men 309-11 welfare system 11 and Second World War 205-6 and First World War 101-2 universities 195 Uriburu, José F. 110 Uruk 31 US Army Corps of Engineers 183 USSR see Russia/USSR US Steel 349 usury 35-6 utility companies 169 see also energy industry utility and probability 189-90 utopianism 17-18 Value at Risk (VaR) models 325 . Vatican 42 Veblen, Thorstein 348 Velasco, Carmen 279 Venezuela 26 Venice 33-8 and bonds 72-3 ghetto 34 Medici in 42 and money-lending 33-8 and Oriental influences 33 San Moise 126 Vernon S&L 255 Versailles Treaty 102-3 Vicksburg 92 Victoria, Queen 238 Vienna 101 Vietnam War 307n.

International banks are required to hold capital equal to 8 per cent of their risk-weighted assets. Basel II, first published in 2004 but only gradually being adopted around the world, sets out more complex rules, distinguishing between credit risk, operational risk and market risk, the last of which mandates the use of value at risk (VaR) models. Ironically, in the light of 2007-8, liquidity risk is combined with other risks under the heading ‘residual risk’. Such rules inevitably conflict with the incentive all banks have to minimize their capital and hence raise their return on equity. bm In Andrew Lo’s words: ‘Hedge funds are the Galapagos Islands of finance . . .


pages: 320 words: 87,853

The Black Box Society: The Secret Algorithms That Control Money and Information by Frank Pasquale

Adam Curtis, Affordable Care Act / Obamacare, Alan Greenspan, algorithmic trading, Amazon Mechanical Turk, American Legislative Exchange Council, asset-backed security, Atul Gawande, bank run, barriers to entry, basic income, Bear Stearns, Berlin Wall, Bernie Madoff, Black Swan, bonus culture, Brian Krebs, business cycle, business logic, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chelsea Manning, Chuck Templeton: OpenTable:, cloud computing, collateralized debt obligation, computerized markets, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, data science, Debian, digital rights, don't be evil, drone strike, Edward Snowden, en.wikipedia.org, Evgeny Morozov, Fall of the Berlin Wall, Filter Bubble, financial engineering, financial innovation, financial thriller, fixed income, Flash crash, folksonomy, full employment, Gabriella Coleman, Goldman Sachs: Vampire Squid, Google Earth, Hernando de Soto, High speed trading, hiring and firing, housing crisis, Ian Bogost, informal economy, information asymmetry, information retrieval, information security, interest rate swap, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, John Bogle, Julian Assange, Kevin Kelly, Kevin Roose, knowledge worker, Kodak vs Instagram, kremlinology, late fees, London Interbank Offered Rate, London Whale, machine readable, Marc Andreessen, Mark Zuckerberg, Michael Milken, mobile money, moral hazard, new economy, Nicholas Carr, offshore financial centre, PageRank, pattern recognition, Philip Mirowski, precariat, profit maximization, profit motive, public intellectual, quantitative easing, race to the bottom, reality distortion field, recommendation engine, regulatory arbitrage, risk-adjusted returns, Satyajit Das, Savings and loan crisis, search engine result page, shareholder value, Silicon Valley, Snapchat, social intelligence, Spread Networks laid a new fibre optics cable between New York and Chicago, statistical arbitrage, statistical model, Steven Levy, technological solutionism, the scientific method, too big to fail, transaction costs, two-sided market, universal basic income, Upton Sinclair, value at risk, vertical integration, WikiLeaks, Yochai Benkler, zero-sum game

Senate Permanent Subcommittee on Investigations, JPMorgan Chase Whale Trades: A Case History of Derivatives Risks and Abuses (2013), 8. (“In the case of the CIO VaR, after analysts concluded the existing model was too conservative and overstated risk, an alternative CIO model was hurriedly adopted in late January 2012, while the CIO was in breach of its own and the bankwide VaR limit. The CIO’s new model immediately lowered the SCP’s VaR by 50%, enabling the CIO not only to end its breach, but to engage in substantially more risky derivatives trading. Months later, the bank determined that the model was improperly implemented, requiring error-prone manual data entry and incorporating formula and calculation errors.”) 106.

“Financial engineers” crafted “swaps” of risk,55 encouraging quants (and regulators) to try to estimate it in ever more precise ways.56 A credit default swap (CDS), for instance, transfers the risk of nonpayment to a third party, which promises to pay you (the first party) in case the debtor (the second party) does not.57 This innovation was celebrated as a landmark of “price discovery,” a day-by-day (or even second-by-second) tracking of exactly how likely an entity was to default.58 114 THE BLACK BOX SOCIETY As with credit scores, the risk modeling here was deeply fallible, another misapplication of natural science methods to an essentially social science of finance. “Value at Risk” models purported to predict with at least 95 percent certainty how much a firm could lose if market prices changed. But the models had to assume the stability of certain kinds of human behavior, which could change in response to widespread adoption of the models themselves. Furthermore, many models gave little weight to the possibility that housing prices would fall across the nation.


pages: 77 words: 18,414

How to Kick Ass on Wall Street by Andy Kessler

Andy Kessler, Bear Stearns, Bernie Madoff, buttonwood tree, call centre, collateralized debt obligation, eat what you kill, family office, fixed income, hiring and firing, invention of the wheel, invisible hand, London Whale, low interest rates, margin call, NetJets, Nick Leeson, pets.com, risk tolerance, Silicon Valley, sovereign wealth fund, time value of money, too big to fail, value at risk

Maybe the smartest get fooled the most. Markets get irrational. Every trader worth their salt knows this or learns it quickly. The old adage is that the market can stay irrational longer than you can stay solvent. This is why firms put trading limits on traders or cumulatively trading desks. There is a firm-wide limit, known as VaR or Value at Risk – the most a firm could lose on any given day. The better you get trading, the higher your personal limit and the bigger chunk of the firm’s capital you get to play with and turn into more money (or end up as a smoking hole in the ground, see Nick Leeson and JP Morgan’s London Whale.) Bond trading is a little different, but not much.


pages: 554 words: 158,687

Profiting Without Producing: How Finance Exploits Us All by Costas Lapavitsas

Alan Greenspan, Andrei Shleifer, asset-backed security, bank run, banking crisis, Basel III, Bear Stearns, borderless world, Branko Milanovic, Bretton Woods, business cycle, capital controls, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, computer age, conceptual framework, corporate governance, credit crunch, Credit Default Swap, David Graeber, David Ricardo: comparative advantage, disintermediation, diversified portfolio, Erik Brynjolfsson, eurozone crisis, everywhere but in the productivity statistics, false flag, financial deregulation, financial independence, financial innovation, financial intermediation, financial repression, Flash crash, full employment, general purpose technology, Glass-Steagall Act, global value chain, global village, High speed trading, Hyman Minsky, income inequality, inflation targeting, informal economy, information asymmetry, intangible asset, job satisfaction, joint-stock company, Joseph Schumpeter, Kenneth Rogoff, liberal capitalism, London Interbank Offered Rate, low interest rates, low skilled workers, M-Pesa, market bubble, means of production, Minsky moment, Modern Monetary Theory, Money creation, money market fund, moral hazard, mortgage debt, Network effects, new economy, oil shock, open economy, pensions crisis, post-Fordism, Post-Keynesian economics, price stability, Productivity paradox, profit maximization, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, race to the bottom, regulatory arbitrage, reserve currency, Robert Shiller, Robert Solow, savings glut, Scramble for Africa, secular stagnation, shareholder value, Simon Kuznets, special drawing rights, Thales of Miletus, The Chicago School, The Great Moderation, the payments system, The Wealth of Nations by Adam Smith, Tobin tax, too big to fail, total factor productivity, trade liberalization, transaction costs, union organizing, value at risk, Washington Consensus, zero-sum game

Heavy involvement of banks in financial transactions meant new dangers of losses due to changes in asset prices; these gave rise to ‘market risk’. A key step in the development of the Basel Accords, therefore, was the introduction of the Amendment of 1996 which made provision for market risk.22 For the largest banks this meant the introduction of advanced models of calculating risk based on value at risk (VaR). The VaR approach simulates changes in the market value of a bank’s portfolio and calculates a capital requirement based upon possible mark-to-market losses.23 Bank balance sheets thus began continually to reflect the movement of securities prices in the open markets, a factor that proved important in the unfolding of the crisis of 2007.

Arm’s-length assessment of borrowers, for instance, has been deployed in judging the risk of mortgages in the US, including ‘credit scoring’ of individuals based on numerical information (income, age, assets, etc) that could be manipulated statistically.31 The risk of default on assets has been more generally assessed via quantitative models that utilize historical rates of default; estimates were largely extrapolations from past trends, stress-tested within limits indicated by data. Banks, as was discussed in the previous section, have also deployed value-at-risk methods to assess the probability that the value of their assets would decline below a certain level, relying on correlations between asset prices and volatility. VaR methods have made it imperative to adopt the accounting practice of ‘marking to market’ – to use current market valuations rather than historic prices. These practices have become officially incorporated in market-conforming regulation, thus gaining further influence among banks.

Saez, Emmanuel, ‘Income and Wealth Concentration in a Historical and International Perspective’, in Public Policy and the Income Distribution, ed. Alan Auerbach, David Card, and John M. Quigley, NY: Russell Sage Foundation, 2006, pp. 221–58. Saunders, Anthony, and Linda Allen, Credit Risk Measurement: New Approaches to Value at Risk and Other Paradigms, 2nd ed., New York: John Wiley and Sons, 2002. Savage, Mike, and Karel Williams (eds), Remembering Elites, London: John Wiley and Sons, 2008. Sawyer, Malcolm, ‘The NAIRU: A Critical Appraisal’, International Papers in Political Economy 6:2, 1999, pp. 1–40; reprinted in Money, Finance and Capitalist Development, ed.


The End of Accounting and the Path Forward for Investors and Managers (Wiley Finance) by Feng Gu

active measures, Affordable Care Act / Obamacare, Alan Greenspan, barriers to entry, book value, business cycle, business process, buy and hold, carbon tax, Claude Shannon: information theory, Clayton Christensen, commoditize, conceptual framework, corporate governance, creative destruction, Daniel Kahneman / Amos Tversky, discounted cash flows, disruptive innovation, diversified portfolio, double entry bookkeeping, Exxon Valdez, financial engineering, financial innovation, fixed income, geopolitical risk, hydraulic fracturing, index fund, information asymmetry, intangible asset, inventory management, Joseph Schumpeter, junk bonds, Kenneth Arrow, knowledge economy, moral hazard, new economy, obamacare, quantitative easing, quantitative trading / quantitative finance, QWERTY keyboard, race to the bottom, risk/return, Robert Shiller, Salesforce, shareholder value, Steve Jobs, tacit knowledge, The Great Moderation, value at risk

Strategic Resources & Consequences Report: Case No. 2 159 The Resource Preservation part of the Resources & Consequences Report (mid-column) should accordingly provide sufficient information enabling investors to evaluate the effectiveness of the company’s risk management, and the extent of risk exposure. Narrative, but not boilerplate, discussion of management’s risk mitigation strategies, with quantitative indicators, like proportion of exposure and premium ceded to reinsurers, along with traditional risk measures, such as VAR (value at risk) should be provided in the Resources & Consequences Report. As for regulatory risk, relevant information includes the status of major rate increase applications and regulators’ moves to impose new coverage on the company. Here, as elsewhere, it’s important to perceive the proposed Report as an integrated system, rather than a list of disparate indicators.

Such price gyrations strongly affect companies’ strategy and financial results—Apache (February 25, 2015, presentation) reported that the recent 190 SO, WHAT’S TO BE DONE? 38 percent oil price decrease caused a 17 percent decline of cash flows—and puts heavy pressure on exploration and production decisions (shutting off operations when prices drop below breakeven?). It is important, therefore, to provide investors with quantitative risk indicators, akin to VAR (value at risk) financial measures, to indicate the sensitivity of cash flows and sales to expected changes in the prices of oil and gas. You surely don’t have to warn investors that oil and gas price volatility affects operations; they know it. But how about quantifying for them the sensitivity of operations to prospective price changes, allowing investors to assess the riskiness of operations and the company’s future growth?


pages: 367 words: 97,136

Beyond Diversification: What Every Investor Needs to Know About Asset Allocation by Sebastien Page

Andrei Shleifer, asset allocation, backtesting, Bernie Madoff, bitcoin, Black Swan, Bob Litterman, book value, business cycle, buy and hold, Cal Newport, capital asset pricing model, commodity super cycle, coronavirus, corporate governance, COVID-19, cryptocurrency, currency risk, discounted cash flows, diversification, diversified portfolio, en.wikipedia.org, equity risk premium, Eugene Fama: efficient market hypothesis, fixed income, future of work, Future Shock, G4S, global macro, implied volatility, index fund, information asymmetry, iterative process, loss aversion, low interest rates, market friction, mental accounting, merger arbitrage, oil shock, passive investing, prediction markets, publication bias, quantitative easing, quantitative trading / quantitative finance, random walk, reserve currency, Richard Feynman, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, robo advisor, seminal paper, shareholder value, Sharpe ratio, sovereign wealth fund, stochastic process, stochastic volatility, stocks for the long run, systematic bias, systematic trading, tail risk, transaction costs, TSMC, value at risk, yield curve, zero-coupon bond, zero-sum game

There are several other modified versions of mean-variance optimization that account for nonnormal (non-Gaussian) higher moments, in particular fat tails.3 For example, we can replace volatility with a measure of tail risk. In Chapter 10 we discussed conditional value at risk (CVaR), a measure of expected loss during market sell-offs. If we use CVaR as our measure of tail risk, we can modify mean variance to solve for the weights that maximize4 – risk aversion × CVaR CVaR can be replaced with any tail-risk measure of choice, such as probability of a loss greater than –10%, value at risk (VaR), etc. We can also use tail-risk constraints. For example, suppose we don’t want CVaR to go below –10%. We can define the optimization problem as Expected return – risk aversion × volatility subject to CVaR <–10% In this framework, any constraint should work.

Exposure to Loss Is What Matters Volatility, skewness, kurtosis, and correlation are the key building blocks for risk forecasting. But ultimately, most investors care about exposure to loss. Conditional value at risk (CVaR), for example, is a popular risk measure. It provides an estimate of the average loss size during sell-offs, usually defined as the bottom 1, 5, or 10% of outcomes. For example, an annual CVaR of –10% at the 5% confidence level means that once every 20 years, we can expect to lose about 10% on our investment. The literature on value-at-risk forecasting and other measures of exposure to loss is obviously related to—and as rich as—the literature on volatility forecasting.

This quote isn’t as well-known, but it speaks to within-horizon exposure to loss. The authors introduce two new risk measures based on first-passage probabilities. Within-horizon probability of loss, noted above, is the probability that the asset or portfolio dips below a threshold at some time, and continuous value at risk is an improved version of value at risk (minimum loss at a given confidence level) that measures exposure to loss throughout the investment horizon. The authors present several examples of why within-horizon risk matters: an asset manager might get fired if performance dips below a threshold; a hedge fund might face insolvency even if the losses are temporary; a borrower must maintain a given level of reserves per the loan covenants; etc.


pages: 309 words: 95,495

Foolproof: Why Safety Can Be Dangerous and How Danger Makes Us Safe by Greg Ip

Affordable Care Act / Obamacare, Air France Flight 447, air freight, airport security, Alan Greenspan, Asian financial crisis, asset-backed security, bank run, banking crisis, Bear Stearns, behavioural economics, Boeing 747, book value, break the buck, Bretton Woods, business cycle, capital controls, central bank independence, cloud computing, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, Daniel Kahneman / Amos Tversky, diversified portfolio, double helix, endowment effect, Exxon Valdez, Eyjafjallajökull, financial deregulation, financial innovation, Financial Instability Hypothesis, floating exchange rates, foreign exchange controls, full employment, global supply chain, hindsight bias, Hyman Minsky, Joseph Schumpeter, junk bonds, Kenneth Rogoff, lateral thinking, Lewis Mumford, London Whale, Long Term Capital Management, market bubble, Michael Milken, money market fund, moral hazard, Myron Scholes, Network effects, new economy, offshore financial centre, paradox of thrift, pets.com, Ponzi scheme, proprietary trading, quantitative easing, Ralph Nader, Richard Thaler, risk tolerance, Ronald Reagan, Sam Peltzman, savings glut, scientific management, subprime mortgage crisis, tail risk, technology bubble, TED Talk, The Great Moderation, too big to fail, transaction costs, union organizing, Unsafe at Any Speed, value at risk, William Langewiesche, zero-sum game

Millions of people become sick or die each year because excessive use of antibiotics causes bacteria to mutate into resistant strains. The systems we’ve developed to learn from history can unintentionally magnify this tendency. Financial institutions, for example, monitor their risk with a formula called “value at risk,” or VaR. Vastly simplified, VaR asks how much money would be lost if securities or interest rates fluctuate as much as they did at their most volatile moment in the recent past. A long period of calm will thus naturally lead a bank to raise its exposure. As that exposure grows, so does the potential loss if volatility exceeds expectations.


pages: 405 words: 109,114

Unfinished Business by Tamim Bayoumi

Alan Greenspan, algorithmic trading, Asian financial crisis, bank run, banking crisis, Basel III, battle of ideas, Bear Stearns, behavioural economics, Ben Bernanke: helicopter money, Berlin Wall, Big bang: deregulation of the City of London, book value, Bretton Woods, British Empire, business cycle, buy and hold, capital controls, Celtic Tiger, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, credit crunch, currency manipulation / currency intervention, currency peg, Doha Development Round, facts on the ground, Fall of the Berlin Wall, financial deregulation, floating exchange rates, full employment, Glass-Steagall Act, Greenspan put, hiring and firing, housing crisis, inflation targeting, junk bonds, Just-in-time delivery, Kenneth Rogoff, liberal capitalism, light touch regulation, London Interbank Offered Rate, Long Term Capital Management, market bubble, Martin Wolf, moral hazard, oil shale / tar sands, oil shock, price stability, prisoner's dilemma, profit maximization, quantitative easing, race to the bottom, random walk, reserve currency, Robert Shiller, Rubik’s Cube, Savings and loan crisis, savings glut, technology bubble, The Great Moderation, The Myth of the Rational Market, the payments system, The Wisdom of Crowds, too big to fail, trade liberalization, transaction costs, value at risk

The awkwardly named Basel 2.5 system was an emergency fix that shored up capital charges for market risk. One reason that the internal risk models used in Basel 2 had generated inadequate capital buffers was that they used historical patterns of shocks and correlations across asset prices. Basel 2.5 moved the system from these value-at-risk models (VaRs) to stressed value-at-risk models (SVaRs) which incorporate the larger financial shocks, higher correlations across asset prices, wider margins between the buy and sell prices of assets, and the higher default risks seen in times of market panic. Some specific provisions for securitized assets, whose markets had largely seized up in the immediate aftermath of the Lehman Brothers bankruptcy, topped off this emergency revamp of capital buffers for market risk.

., (i) Roubini, Nouriel, (i) Royal Bank of Scotland (RBS; UK bank), (i) Russia exchange rate collapse (1998), (i) joins WTO, (i) safe haven bankruptcy protection (US), (i) Sanio, Jochen, (i) Santander (Spanish bank) assets expanded, (i) capitalization, (i) international scope, (i) as mega-bank, (i) takeovers, (i) Sants, Hector, (i) Savings and Loans (US), (i), (ii) Scandinavia: monetary union, (i) Schmidt, Helmut, (i), (ii), (iii) Schoales, Myron, (i) Securities and Exchange Commission (SEC; US) and mortgage-backed securities, (i) registers hedge funds, (i) and regulation, (i), (ii), (iii), (iv) Release 47683 widens repurchase agreement collateral, (i) and repo market, (i), (ii), (iii), (iv) securitization and mortgages, (i), (ii), (iii), (iv), (v), (vi) private label, (i) in US, (i), (ii), (iii), (iv), (v), (vi) Security Pacific Corporation (US bank), (i) shadow banks see United States of America share prices: fluctuations and predictions, (i), (ii) Shiller, Robert, (i), (ii), (iii) Silva-Herzog, Jesus, (i) silver: in US money supply, (i) single currency benefits, (i) effect on trade, (i) and macroeconomic shocks, (i) see also Euro area Single European Act (1986), (i), (ii), (iii), (iv), (v) Smith, Adam, (i), (ii) Smithsonian Agreement, (i), (ii) snake currency arrangement (Europe), (i), (ii) Société Générale (French bank): expansion, (i), (ii), (iii) South Korea, (i), (ii), (iii), (iv) Spain borrowing interest rate, (i) caja savings banks, (i) commercial loans, (i) in currency union periphery, (i) ejected from Exchange Rate Mechanism, (i) excessive borrowing, (i) in Exchange Rate Mechanism, (i) expansion in bank assets, (i) and ESM funding to restructure banking system, (i) financial crisis in, (i), (ii), (iii) high interest rates, (i) housing, (i), (ii) included in Euro area, (i) local governments in, (i) reduces fiscal deficit, (i) successful effect of reforms, (i) ten-year bonds, (i) Stability and Growth Pact (SGP, Euro area), (i), (ii), (iii), (iv), (v) Strasbourg summit (of European leaders, 1990), (i), (ii) Strauss-Kahn, Dominique, (i) stressed value-at-risk models (SVARs), (i) Suez (French bank), (i) supply and demand, law of, (i) Sweden in Basel Committee, (i) and currency fluctuations, (i) in Scandinavian monetary union, (i) Switzerland in financial crisis, (i) trade with EMU members, (i) taxes cuts, (i), (ii), (iii), (iv) favor debt over equity, (i) kept low, (i) little effect on private spending, (i) TCW (US bank), (i) Texas: house prices fall, (i) Thailand, (i), (ii), (iii), (iv) Thatcher, Margaret, (i), (ii), (iii), (iv), (v), (vi) trade: affected by single currency, (i) trade balance, (i) Travelers Group (US financial institution), (i) Trichet, Jean-Claude, (i) Trump, Donald elected President, (i) and fiscal stimulus, (i) looser view on bank regulation, (i) proposes tax cuts, (i) UBS (Swiss bank), (i) UniCredit (Italian bank), (i), (ii), (iii) United Kingdom (Britain) bank assets reduced since 2008, (i) banking expansion, (i), (ii) banking system, (i) bond markets, (i) central bank independence, (i) common capital standard agreed with US, (i) core Euro banks expand into, (i) and currency fluctuations, (i) favors larger bank capital buffers, (i) favours EU-wide bank regulator, (i) financial crisis (1866), (i) foreign banks in, (i) foreign investments in, (i) high inflation, (i), (ii) invited to join European Economic Community, (i) joins Exchange Rate Mechanism, (i) large outflows, (i) leaves European Union, (i) leaves Exchange Rate Mechanism, (i), (ii) ‘light touch’ regulation, (i), (ii), (iii), (iv), (v) in North Atlantic financial crisis (2008), (i) opts out of Maastricht Treaty, (i) owns US assets, (i), (ii) product market, (i) rebate from EEC budget, (i) resists monetary union, (i), (ii) scale of banking, (i) separated commercial and investment (merchant) banks, (i) trade with EMU members, (i) see also pound sterling United States of America accepts Basel 3 framework for large banks, (i) accounting practices, (i) adopts new leverage ratio, (i) aggregate spending, (i) anchor regions and peripheries in currency union, (i) and Asian crisis, (i) assets held by European banks, (i), (ii) bank assets reduced since 2008, (i) bank deposits migrate, (i) bank failures and prompt corrective action, (i) bank mergers, (i) bank size compared with Europe, (i) bankers’ morality, (i) banking expansion, (i) banking regulation, (i), (ii), (iii), (iv), (v), (vi), (vii) and Basel 2 accord, (i) in Basel Committee, (i) bond markets, (i) bond yields fall, (i) business cycles, (i) champions internal risk models, (i) common capital standard agreed with UK, (i) consumer price index, (i) core Euro banks expand into, (i) as crisis country, (i) currency as international standard, (i) currency union in, (i), (ii), (iii) debt outflows, (i) deregulation, (i), (ii) devaluation, (i) effect of break-up of Bretton Woods on, (i) effect of post-crisis changes, (i), (ii) Euro area lends to, (i), (ii) European universal banks in, (i), (ii) favors larger bank capital buffers, (i) federal support for banks, (i) federal tax system, (i) financial boom, (i), (ii) financial reform in, (i), (ii) financial system (2002), (i), (ii) floating exchange rates, (i) Flow of Funds data, (i), (ii) fractured banking system, (i) and gold market, (i) high tech boom collapses, (i) house prices, (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x), (xi), (xii), (xiii), (xiv), (xv) imposes surcharge on foreign imports, (i) improved monetary policy, (i) inflation fluctuates, (i), (ii), (iii), (iv), (v), (vi) integrated banking system, (i) interest rates limited, (i), (ii), (iii) investment bank expansion, (i), (ii) investment bank regulation, (i), (ii), (iii) labor market flexibility, (i) and Latin American debt crisis, (i), (ii) misery index, (i) modest recovery from crisis, (i) national (interstate) banks, (i), (ii), (iii), (iv) national price movements, (i) and oil prices, (i) output volatility, (i) policy coordination fades, (i) post 2002 financial boom, (i) product market, (i) recessions (1985–2005), (i) regulation of shadow banks, (i) and repo market, (i) response to crisis, (i) responsibility for macroprudential policies, (i) and risk measures, (i), (ii) securitization, (i), (ii) separates commercial and investment banking, (i), (ii), (iii) shadow banks develop, (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x) and small bank regulation, (i) trade balance, (i), (ii) trade with EMU members, (i) unprepared for financial crisis, (i) United States Federal Reserve Bank belief in market discipline of investment banks, (i), (ii), (iii), (iv), (v) and business cycle, (i) conducts stress tests, (i) cooperation of monetary and fiscal policy, (i) eases rates, (i), (ii) easy financing conditions, (i) emergency funding, (i), (ii) faith in investors’ judgment, (i) helps stabilize markets, (i) and house price boom, (i) and inflation rates, (i) monetary policy, (i) as proposed model for European Central Bank, (i) provides safety net, (i), (ii) regulates mortgage lending standards, (i) and regulation of investment banks, (i), (ii) Regulation Q, (i) regulatory function and practice, (i), (ii), (iii), (iv) response to crisis, (i) and risk models, (i), (ii), (iii) and tax cuts, (i) urges reform of Basel (i), (ii) warns about loans to Latin America, (i) value-at-risk models (VARs), (i) Venezuela, (i) Versailles Treaty (1919), (i) Vietnam War, (i) Volcker, Paul, (i), (ii) rule, (i) Wachovia Corporation (US bank), (i) Wall Street: reform, (i) waterfall investment structures, (i) welfare payments, (i) Wells Fargo (US bank), (i), (ii) Werner Commission Report (Europe) (1970), (i), (ii), (iii) West Germany economic growth, (i) in European Coal and Steel Community, (i) see also Germany White, Bill, (i) White, Harry Dexter, (i) won (S.

Securities would be classified on standardized measures of the risk of large changes in prices—basically the same “bucket” approach that already applied to credit risk. To the surprise of the Committee, this proposal ran into strong criticism. This came mainly from the large banks, who argued that the proposed buckets were much less sophisticated than their own rapidly evolving “value-at-risk” models that calculated the risk to the value of an entire portfolio by taking into account not simply the volatility of individual asset prices but also the correlations across such prices. They noted that adopting the Committee’s proposal would lessen incentives to continue to develop their own internal risk models.


pages: 393 words: 115,263

Planet Ponzi by Mitch Feierstein

Affordable Care Act / Obamacare, Alan Greenspan, Albert Einstein, Asian financial crisis, asset-backed security, bank run, banking crisis, barriers to entry, Bear Stearns, Bernie Madoff, book value, break the buck, centre right, collapse of Lehman Brothers, collateralized debt obligation, commoditize, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, disintermediation, diversification, Donald Trump, energy security, eurozone crisis, financial innovation, financial intermediation, fixed income, Flash crash, floating exchange rates, frictionless, frictionless market, Future Shock, Glass-Steagall Act, government statistician, high net worth, High speed trading, illegal immigration, income inequality, interest rate swap, invention of agriculture, junk bonds, light touch regulation, Long Term Capital Management, low earth orbit, low interest rates, mega-rich, money market fund, moral hazard, mortgage debt, negative equity, Neil Armstrong, Northern Rock, obamacare, offshore financial centre, oil shock, pensions crisis, plutocrats, Ponzi scheme, price anchoring, price stability, proprietary trading, purchasing power parity, quantitative easing, risk tolerance, Robert Shiller, Ronald Reagan, tail risk, too big to fail, trickle-down economics, value at risk, yield curve

And stick close to actual market pricing, because only then do you stay close to reality. Long tail risk There’s a variant on risk quantification which carries its own separate hazards. Back in the 1990s, JP Morgan‌—‌then and now, one of the best-run banks on the market‌—‌invented a risk management technology which measured ‘value at risk’ or VAR. That is, it could tell you how much money you would stand to lose on your entire portfolio if interest rates rose a little, or if the yen fell a little, and so on. The technology was a terrific innovation, and became widespread across the market. But it had a limitation. It could only predict likely losses under likely scenarios.

Of course, that’s information any decently managed financial institution needs‌—‌an essential part of the information that enables it to manage its ordinary risks as accurately as possible. Ninety-nine times out of a hundred, when you come into work, you won’t find a tornado flying around your dealing room. But ordinary risks are not the risks which are going to bury your firm‌—‌and the whole of Western capitalism‌—‌under a mountain of excessive debts and lousy assets. VAR technology was so dangerous because the technology was so good‌—‌95% of the time. As it happened, JP Morgan was never suckered by its own creation. The firm remembered what it could do and what it could not do. Management wanted to build a ‘fortress balance sheet’ that would withstand the 1 in 10,000 chance, as well as the 95 in 100 one.


pages: 491 words: 131,769

Crisis Economics: A Crash Course in the Future of Finance by Nouriel Roubini, Stephen Mihm

Alan Greenspan, Asian financial crisis, asset-backed security, balance sheet recession, bank run, banking crisis, barriers to entry, Bear Stearns, behavioural economics, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, bond market vigilante , bonus culture, Bretton Woods, BRICs, British Empire, business cycle, call centre, capital controls, Carmen Reinhart, central bank independence, centralized clearinghouse, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, dark matter, David Ricardo: comparative advantage, debt deflation, Eugene Fama: efficient market hypothesis, Fall of the Berlin Wall, fiat currency, financial deregulation, financial engineering, financial innovation, Financial Instability Hypothesis, financial intermediation, full employment, George Akerlof, Glass-Steagall Act, global pandemic, global reserve currency, Gordon Gekko, Greenspan put, Growth in a Time of Debt, housing crisis, Hyman Minsky, information asymmetry, interest rate swap, invisible hand, Joseph Schumpeter, junk bonds, Kenneth Rogoff, laissez-faire capitalism, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, Louis Bachelier, low interest rates, margin call, market bubble, market fundamentalism, Martin Wolf, means of production, Minsky moment, money market fund, moral hazard, mortgage debt, mortgage tax deduction, new economy, Northern Rock, offshore financial centre, oil shock, Paradox of Choice, paradox of thrift, Paul Samuelson, Ponzi scheme, price stability, principal–agent problem, private sector deleveraging, proprietary trading, pushing on a string, quantitative easing, quantitative trading / quantitative finance, race to the bottom, random walk, regulatory arbitrage, reserve currency, risk tolerance, Robert Shiller, Satyajit Das, Savings and loan crisis, savings glut, short selling, South Sea Bubble, sovereign wealth fund, special drawing rights, subprime mortgage crisis, Suez crisis 1956, The Great Moderation, The Myth of the Rational Market, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, too big to fail, tulip mania, Tyler Cowen, unorthodox policies, value at risk, We are all Keynesians now, Works Progress Administration, yield curve, Yom Kippur War

Again, this technique for managing liquidity risk won’t solve everything: as the recent crisis demonstrated, even institutions that traffic almost exclusively in liquid investments—money market funds, for instance—can still get themselves in trouble by putting just a tiny bit of their money into less liquid assets. Another flaw with the existing Basel regime is that it gives too much discretion to financial institutions to use their own internal models for assessing risk. The Value at Risk model (VaR) is one, a mathematical formula that purports to calculate the likelihood that a firm will suffer a loss on its assets; another is the Gaussian copula, a statistical tool that was used to price esoteric assets like CDOs. Both models disregarded the likelihood of crises and other disruptive events, leaving banks vulnerable when the crisis hit.

underemployment equilibrium unemployment in emerging economies fiscal policy and in Great Depression Keynes’s views on recovery and unemployment benefits United Kingdom Financial Services Authority in future of United States Bretton Woods system and cooperation issue and current account deficit of current account surplus of debt, foreigners’ purchase of decline of demand in as emerging market empire of foreign investment in Great Moderation and housing bubble in housing bust in Latin American debt crisis and in Mexican bailout pandemics and panics in railroads and recovery in Roubini’s predictions for savings and loan crisis in savings in, see savings, U.S. spread of financial crisis from stagflation in trade deficit of uninsured deposits in Unocal Uruguay U.S. Central Value at Risk model (VaR) Van Buren, Martin Vienna Vietnam Vietnam War Volcker, Paul Wachovia wages and income current account and decline in disposable of hedge fund managers from migrant workers rise in see also compensation Wall Street educated workers attracted to greed and Greenspan put and regulators compared with employees of vanishing of Walraséon Washington Mutual Washington Post Wealth of Nations (Smith) Weill, Sanford Wells Fargo WesCorp Western Europe see also specific countries White, William white swans World Bank World War II Yeltsin, Boris yen, Japanese Yom Kippur War yuan, Chinese z ero-interest-rate policy (ZIRP) Zhou Xiaochuan


pages: 505 words: 142,118

A Man for All Markets by Edward O. Thorp

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", 3Com Palm IPO, Alan Greenspan, Albert Einstein, asset allocation, Bear Stearns, beat the dealer, Bernie Madoff, Black Monday: stock market crash in 1987, Black Swan, Black-Scholes formula, book value, Brownian motion, buy and hold, buy low sell high, caloric restriction, caloric restriction, carried interest, Chuck Templeton: OpenTable:, Claude Shannon: information theory, cognitive dissonance, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Edward Thorp, Erdős number, Eugene Fama: efficient market hypothesis, financial engineering, financial innovation, Garrett Hardin, George Santayana, German hyperinflation, Glass-Steagall Act, Henri Poincaré, high net worth, High speed trading, index arbitrage, index fund, interest rate swap, invisible hand, Jarndyce and Jarndyce, Jeff Bezos, John Bogle, John Meriwether, John Nash: game theory, junk bonds, Kenneth Arrow, Livingstone, I presume, Long Term Capital Management, Louis Bachelier, low interest rates, margin call, Mason jar, merger arbitrage, Michael Milken, Murray Gell-Mann, Myron Scholes, NetJets, Norbert Wiener, PalmPilot, passive investing, Paul Erdős, Paul Samuelson, Pluto: dwarf planet, Ponzi scheme, power law, price anchoring, publish or perish, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, RFID, Richard Feynman, risk-adjusted returns, Robert Shiller, rolodex, Sharpe ratio, short selling, Silicon Valley, Stanford marshmallow experiment, statistical arbitrage, stem cell, stock buybacks, stocks for the long run, survivorship bias, tail risk, The Myth of the Rational Market, The Predators' Ball, the rule of 72, The Wisdom of Crowds, too big to fail, Tragedy of the Commons, uptick rule, Upton Sinclair, value at risk, Vanguard fund, Vilfredo Pareto, Works Progress Administration

We also offset the danger to the portfolio from sudden large shifts in overall stock market prices and in the volatility level of the market. From the 1980s on, some of these techniques came into usage by modern investment banks and hedge funds. They also adopted a notion we rejected, called VaR or “value at risk,” where they estimated the damage to their portfolio for, say, the worst events among the most likely 95 percent of future outcomes, neglecting the extreme 5 percent “tails,” then acted to reduce any unacceptably large risks. The defect of VaR alone is that it doesn’t fully account for the worst 5 percent of expected cases. But these extreme events are where ruin is to be found. It’s also true that extreme changes in securities prices may be much greater than you would expect from the Gaussian or normal statistics commonly used.


pages: 1,202 words: 424,886

Stigum's Money Market, 4E by Marcia Stigum, Anthony Crescenzi

accounting loophole / creative accounting, Alan Greenspan, Asian financial crisis, asset allocation, asset-backed security, bank run, banking crisis, banks create money, Bear Stearns, Black-Scholes formula, book value, Brownian motion, business climate, buy and hold, capital controls, central bank independence, centralized clearinghouse, corporate governance, credit crunch, Credit Default Swap, cross-border payments, currency manipulation / currency intervention, currency risk, David Ricardo: comparative advantage, disintermediation, distributed generation, diversification, diversified portfolio, Dutch auction, financial innovation, financial intermediation, fixed income, flag carrier, foreign exchange controls, full employment, Glass-Steagall Act, Goodhart's law, Greenspan put, guns versus butter model, high net worth, implied volatility, income per capita, intangible asset, interest rate derivative, interest rate swap, inverted yield curve, junk bonds, land bank, large denomination, locking in a profit, London Interbank Offered Rate, low interest rates, margin call, market bubble, market clearing, market fundamentalism, Money creation, money market fund, mortgage debt, Myron Scholes, offshore financial centre, paper trading, pension reform, Phillips curve, Ponzi scheme, price mechanism, price stability, profit motive, proprietary trading, prudent man rule, Real Time Gross Settlement, reserve currency, risk free rate, risk tolerance, risk/return, Savings and loan crisis, seigniorage, shareholder value, short selling, short squeeze, tail risk, technology bubble, the payments system, too big to fail, transaction costs, two-sided market, value at risk, volatility smile, yield curve, zero-coupon bond, zero-sum game

For example, if the bank’s foreign-exchange trading operation is exceeding its risk limit, that added risk reduces the amount of risk that other areas of the bank can take—that is, if the bank wants to allow the foreign-exchange department to take the added risk in this instance and for overall risks to stay within the confines of the risks that the bank previously dictated it should take. Value at risk (VAR) is an example of a risk metric that banks use as a means of determining the amount of risk it is exposed to. VAR takes a probabilistic approach to measuring the risks to a portfolio associated with market volatility. One of the metrics that can be deployed in a VAR model is historical volatility, which basically looks at the typical percentage changes that occur in a financial instrument over a period of time. The Basel Committee on Banking Supervision implemented market-capital risk requirements on banks based on VAR analyses, giving banks the option of using their own VAR systems under certain conditions.

The Basel Committee on Banking Supervision implemented market-capital risk requirements on banks based on VAR analyses, giving banks the option of using their own VAR systems under certain conditions. A key objective was to address systemic risks that might be posed from the growing use of derivatives. Interest-Rate Futures as a Tool of Gap Management By regulation, banks are supposed to use futures only to hedge. But in point of fact, banks have so many assets and liabilities on their books that they can defend almost any position they might take in futures, from the 3-month Eurodollar to the T-bond contract, as a hedge. Consequently, bankers regard and use futures as one more tool of gap management. If a bank wants to be long, it may at times make more sense for it to buy futures than for it to buy cash instruments.


pages: 351 words: 102,379

Too big to fail: the inside story of how Wall Street and Washington fought to save the financial system from crisis--and themselves by Andrew Ross Sorkin

"World Economic Forum" Davos, affirmative action, Alan Greenspan, Andy Kessler, Asian financial crisis, Bear Stearns, Berlin Wall, book value, break the buck, BRICs, business cycle, Carl Icahn, collapse of Lehman Brothers, collateralized debt obligation, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, deal flow, Dr. Strangelove, Emanuel Derman, Fall of the Berlin Wall, fear of failure, financial engineering, fixed income, Glass-Steagall Act, Goldman Sachs: Vampire Squid, housing crisis, indoor plumbing, invisible hand, junk bonds, Ken Thompson, London Interbank Offered Rate, Long Term Capital Management, low interest rates, margin call, market bubble, Michael Milken, Mikhail Gorbachev, money market fund, moral hazard, naked short selling, NetJets, Northern Rock, oil shock, paper trading, proprietary trading, risk tolerance, Robert Shiller, rolodex, Ronald Reagan, Savings and loan crisis, savings glut, shareholder value, short selling, sovereign wealth fund, supply-chain management, too big to fail, uptick rule, value at risk, éminence grise

The real question about Goldman’s success, which could be asked about other firms as well, is this: How should regulators respond to continued risk taking—which generates enormous profits—when the government and taxpayers provide an implicit, if not explicit, guarantee of its business? Indeed, in Goldman’s second quarter of 2009, its VaR, or value at risk, on any given day had risen to an all-time high of $245 million. (A year earlier that figure had been $184 million.) Goldman’s trades have so far paid off, but what if it had bet the wrong way? For better or worse, Goldman, like so many of the nation’s largest financial institutions, remains too big to fail.

“great vampire squid wrapped”: Matt Taibbi, “The Great American Bubble Machine,” Rolling Stone, July 13, 2009. Goldman reported a profit of $5.2 billion: On April 13, Goldman reported net earnings of $1.81 billion for its first quarter. Three months later, its second-quarter earnings soared to $3.44 billion. See http://www2.goldmansachs.com. Goldman’s VaR rising to record high: Christine Harper, “Goldman Sachs VaR Reaches Record on Risks Led by Equity Trading,” Bloomberg, July 15, 2009. “emergency actions meant to provide confidence”: Department of the Treasury press release, “Secretary Geithner Introduces Financial Stability Plan,” February 10, 2009. See http://www.treasury.gov/press/releases/tg18.htm.


Hedgehogging by Barton Biggs

activist fund / activist shareholder / activist investor, Alan Greenspan, asset allocation, backtesting, barriers to entry, Bear Stearns, Big Tech, book value, Bretton Woods, British Empire, business cycle, buy and hold, diversification, diversified portfolio, eat what you kill, Elliott wave, family office, financial engineering, financial independence, fixed income, full employment, global macro, hiring and firing, index fund, Isaac Newton, job satisfaction, junk bonds, low interest rates, margin call, market bubble, Mary Meeker, Mikhail Gorbachev, new economy, oil shale / tar sands, PalmPilot, paradox of thrift, Paul Samuelson, Ponzi scheme, proprietary trading, random walk, Reminiscences of a Stock Operator, risk free rate, Ronald Reagan, secular stagnation, Sharpe ratio, short selling, Silicon Valley, transaction costs, upwardly mobile, value at risk, Vanguard fund, We are all Keynesians now, zero-sum game, éminence grise

The media loved it and published the names of all the supposedly smart, sophisticated individuals and institutions who had lost their money. Everybody was deeply embarrassed, and ever since the big institutions have been obsessed with risk analytics and throw around terms like stress-testing portfolios, value at risk (VAR), and Sharpe ratios. The funds of funds employ sophisticated quantitative analytics to add value by strategically allocating among the different hedge-fund classes.The hedge-fund universe is usually broken down into seven broad investment style classifications.These are event driven, fixed-income arbitrage, global convertible bond arbitrage, equity market-neutral, long/short equity, global macro, and commodity trading funds.


pages: 552 words: 168,518

MacroWikinomics: Rebooting Business and the World by Don Tapscott, Anthony D. Williams

"World Economic Forum" Davos, accounting loophole / creative accounting, airport security, Andrew Keen, augmented reality, Ayatollah Khomeini, barriers to entry, Ben Horowitz, bioinformatics, blood diamond, Bretton Woods, business climate, business process, buy and hold, car-free, carbon footprint, carbon tax, Charles Lindbergh, citizen journalism, Clayton Christensen, clean water, Climategate, Climatic Research Unit, cloud computing, collaborative editing, collapse of Lehman Brothers, collateralized debt obligation, colonial rule, commoditize, corporate governance, corporate social responsibility, creative destruction, crowdsourcing, death of newspapers, demographic transition, digital capitalism, digital divide, disruptive innovation, distributed generation, do well by doing good, don't be evil, en.wikipedia.org, energy security, energy transition, Evgeny Morozov, Exxon Valdez, failed state, fault tolerance, financial innovation, Galaxy Zoo, game design, global village, Google Earth, Hans Rosling, hive mind, Home mortgage interest deduction, information asymmetry, interchangeable parts, Internet of things, invention of movable type, Isaac Newton, James Watt: steam engine, Jaron Lanier, jimmy wales, Joseph Schumpeter, Julian Assange, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, machine readable, Marc Andreessen, Marshall McLuhan, mass immigration, medical bankruptcy, megacity, military-industrial complex, mortgage tax deduction, Netflix Prize, new economy, Nicholas Carr, ocean acidification, off-the-grid, oil shock, old-boy network, online collectivism, open borders, open economy, pattern recognition, peer-to-peer lending, personalized medicine, radical decentralization, Ray Kurzweil, RFID, ride hailing / ride sharing, Ronald Reagan, Rubik’s Cube, scientific mainstream, shareholder value, Silicon Valley, Skype, smart grid, smart meter, social graph, social web, software patent, Steve Jobs, synthetic biology, systems thinking, text mining, the long tail, the scientific method, The Wisdom of Crowds, transaction costs, transfer pricing, University of East Anglia, urban sprawl, value at risk, WikiLeaks, X Prize, Yochai Benkler, young professional, Zipcar

Can investors and others ever again believe the stated profits or losses of any financial institution, its purported capital base and financial soundness, when these numbers are based on secret and opaque models that are derived from mathematics so complex that even the company’s executive management does not understand them? Going forward, the mathematics behind the value and risk calculations for new financial instruments should be open and vetted by a crowd of experts, applying the wisdom of many to the problem. They should know, for example, whether the VaR (Value at Risk) analysis is based on information from only a couple of years, which would not cover the consequences of a once-in-a-generation event. The underlying data and the algorithms for complex derivatives such as collateralized debt obligations should be placed on the Internet, where investors could “fly over” and “drill down” into an instrument’s underlying assets.