{"product_id":"measuring-market-risk-isbn-9780470013038","title":"Measuring Market Risk","description":"Fully revised and restructured, \u003ci\u003eMeasuring Market Risk, Second Edition\u003c\/i\u003e includes a new chapter on options risk management, as well as substantial new information on parametric risk, non-parametric measurements and liquidity risks, more practical information to help with specific calculations, and new examples including Q\u0026amp;A’s and case studies.  \u003cp\u003ePreface to the Second Edition xiii\u003c\/p\u003e \u003cp\u003eAcknowledgements xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 The Rise of Value at Risk 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 The emergence of financial risk management 2\u003c\/p\u003e \u003cp\u003e1.2 Market risk measurement 4\u003c\/p\u003e \u003cp\u003e1.3 Risk measurement before VaR 5\u003c\/p\u003e \u003cp\u003e1.3.1 Gap analysis 5\u003c\/p\u003e \u003cp\u003e1.3.2 Duration analysis 5\u003c\/p\u003e \u003cp\u003e1.3.3 Scenario analysis 6\u003c\/p\u003e \u003cp\u003e1.3.4 Portfolio theory 7\u003c\/p\u003e \u003cp\u003e1.3.5 Derivatives risk measures 8\u003c\/p\u003e \u003cp\u003e1.4 Value at risk 9\u003c\/p\u003e \u003cp\u003e1.4.1 The origin and development of VaR 9\u003c\/p\u003e \u003cp\u003e1.4.2 Attractions of VaR 11\u003c\/p\u003e \u003cp\u003e1.4.3 Criticisms of VaR 13\u003c\/p\u003e \u003cp\u003eAppendix: Types of Market Risk 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Measures of Financial Risk 19\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 The mean–variance framework for measuring financial risk 20\u003c\/p\u003e \u003cp\u003e2.2 Value at risk 27\u003c\/p\u003e \u003cp\u003e2.2.1 Basics of VaR 27\u003c\/p\u003e \u003cp\u003e2.2.2 Determination of the VaR parameters 29\u003c\/p\u003e \u003cp\u003e2.2.3 Limitations of VaR as a risk measure 31\u003c\/p\u003e \u003cp\u003e2.3 Coherent risk measures 32\u003c\/p\u003e \u003cp\u003e2.3.1 The coherence axioms and their implications 32\u003c\/p\u003e \u003cp\u003e2.3.2 The expected shortfall 35\u003c\/p\u003e \u003cp\u003e2.3.3 Spectral risk measures 37\u003c\/p\u003e \u003cp\u003e2.3.4 Scenarios as coherent risk measures 42\u003c\/p\u003e \u003cp\u003e2.4 Conclusions 44\u003c\/p\u003e \u003cp\u003eAppendix 1: Probability Functions 45\u003c\/p\u003e \u003cp\u003eAppendix 2: Regulatory Uses of VaR 52\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Estimating Market Risk Measures: An Introduction and Overview 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Data 53\u003c\/p\u003e \u003cp\u003e3.1.1 Profit\/loss data 53\u003c\/p\u003e \u003cp\u003e3.1.2 Loss\/profit data 54\u003c\/p\u003e \u003cp\u003e3.1.3 Arithmetic return data 54\u003c\/p\u003e \u003cp\u003e3.1.4 Geometric return data 54\u003c\/p\u003e \u003cp\u003e3.2 Estimating historical simulation VaR 56\u003c\/p\u003e \u003cp\u003e3.3 Estimating parametric VaR 57\u003c\/p\u003e \u003cp\u003e3.3.1 Estimating VaR with normally distributed profits\/losses 57\u003c\/p\u003e \u003cp\u003e3.3.2 Estimating VaR with normally distributed arithmetic returns 59\u003c\/p\u003e \u003cp\u003e3.3.3 Estimating lognormal VaR 61\u003c\/p\u003e \u003cp\u003e3.4 Estimating coherent risk measures 64\u003c\/p\u003e \u003cp\u003e3.4.1 Estimating expected shortfall 64\u003c\/p\u003e \u003cp\u003e3.4.2 Estimating coherent risk measures 64\u003c\/p\u003e \u003cp\u003e3.5 Estimating the standard errors of risk measure estimators 69\u003c\/p\u003e \u003cp\u003e3.5.1 Standard errors of quantile estimators 69\u003c\/p\u003e \u003cp\u003e3.5.2 Standard errors in estimators of coherent risk measures 72\u003c\/p\u003e \u003cp\u003e3.6 The core issues: an overview 73\u003c\/p\u003e \u003cp\u003eAppendix 1: Preliminary Data Analysis 75\u003c\/p\u003e \u003cp\u003eAppendix 2: Numerical Integration Methods 80\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Non-parametric Approaches 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Compiling historical simulation data 84\u003c\/p\u003e \u003cp\u003e4.2 Estimation of historical simulation VaR and ES 84\u003c\/p\u003e \u003cp\u003e4.2.1 Basic historical simulation 84\u003c\/p\u003e \u003cp\u003e4.2.2 Bootstrapped historical simulation 85\u003c\/p\u003e \u003cp\u003e4.2.3 Historical simulation using non-parametric density estimation 86\u003c\/p\u003e \u003cp\u003e4.2.4 Estimating curves and surfaces for VAR and ES 88\u003c\/p\u003e \u003cp\u003e4.3 Estimating confidence intervals for historical simulation VaR and ES 89\u003c\/p\u003e \u003cp\u003e4.3.1 An order-statistics approach to the estimation of confidence intervals for HS VaR and ES 89\u003c\/p\u003e \u003cp\u003e4.3.2 A bootstrap approach to the estimation of confidence intervals for HS VaR and ES 90\u003c\/p\u003e \u003cp\u003e4.4 Weighted historical simulation 92\u003c\/p\u003e \u003cp\u003e4.4.1 Age-weighted historical simulation 93\u003c\/p\u003e \u003cp\u003e4.4.2 Volatility-weighted historical simulation 94\u003c\/p\u003e \u003cp\u003e4.4.3 Correlation-weighted historical simulation 95\u003c\/p\u003e \u003cp\u003e4.4.4 Filtered historical simulation 96\u003c\/p\u003e \u003cp\u003e4.5 Advantages and disadvantages of non-parametric methods 99\u003c\/p\u003e \u003cp\u003e4.5.1 Advantages 99\u003c\/p\u003e \u003cp\u003e4.5.2 Disadvantages 100\u003c\/p\u003e \u003cp\u003e4.6 Conclusions 101\u003c\/p\u003e \u003cp\u003eAppendix 1: Estimating Risk Measures with Order Statistics 102\u003c\/p\u003e \u003cp\u003eAppendix 2: The Bootstrap 105\u003c\/p\u003e \u003cp\u003eAppendix 3: Non-parametric Density Estimation 111\u003c\/p\u003e \u003cp\u003eAppendix 4: Principal Components Analysis and Factor Analysis 118\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Forecasting Volatilities, Covariances and Correlations 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Forecasting volatilities 127\u003c\/p\u003e \u003cp\u003e5.1.1 Defining volatility 127\u003c\/p\u003e \u003cp\u003e5.1.2 Historical volatility forecasts 128\u003c\/p\u003e \u003cp\u003e5.1.3 Exponentially weighted moving average volatility 129\u003c\/p\u003e \u003cp\u003e5.1.4 GARCH models 131\u003c\/p\u003e \u003cp\u003e5.1.5 Implied volatilities 136\u003c\/p\u003e \u003cp\u003e5.2 Forecasting covariances and correlations 137\u003c\/p\u003e \u003cp\u003e5.2.1 Defining covariances and correlations 137\u003c\/p\u003e \u003cp\u003e5.2.2 Historical covariances and correlations 138\u003c\/p\u003e \u003cp\u003e5.2.3 Exponentially weighted moving average covariances 140\u003c\/p\u003e \u003cp\u003e5.2.4 GARCH covariances 140\u003c\/p\u003e \u003cp\u003e5.2.5 Implied covariances and correlations 141\u003c\/p\u003e \u003cp\u003e5.2.6 Some pitfalls with correlation estimation 141\u003c\/p\u003e \u003cp\u003e5.3 Forecasting covariance matrices 142\u003c\/p\u003e \u003cp\u003e5.3.1 Positive definiteness and positive semi-definiteness 142\u003c\/p\u003e \u003cp\u003e5.3.2 Historical variance–covariance estimation 142\u003c\/p\u003e \u003cp\u003e5.3.3 Multivariate EWMA 142\u003c\/p\u003e \u003cp\u003e5.3.4 Multivariate GARCH 142\u003c\/p\u003e \u003cp\u003e5.3.5 Computational problems with covariance and correlation matrices 143\u003c\/p\u003e \u003cp\u003eAppendix: Modelling Dependence: Correlations and Copulas 145\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Parametric Approaches (I) 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Conditional vs unconditional distributions 152\u003c\/p\u003e \u003cp\u003e6.2 Normal VaR and ES 154\u003c\/p\u003e \u003cp\u003e6.3 The t-distribution 159\u003c\/p\u003e \u003cp\u003e6.4 The lognormal distribution 161\u003c\/p\u003e \u003cp\u003e6.5 Miscellaneous parametric approaches 165\u003c\/p\u003e \u003cp\u003e6.5.1 Lévy approaches 165\u003c\/p\u003e \u003cp\u003e6.5.2 Elliptical and hyperbolic approaches 167\u003c\/p\u003e \u003cp\u003e6.5.3 Normal mixture approaches 167\u003c\/p\u003e \u003cp\u003e6.5.4 Jump diffusion 168\u003c\/p\u003e \u003cp\u003e6.5.5 Stochastic volatility approaches 169\u003c\/p\u003e \u003cp\u003e6.5.6 The Cornish–Fisher approximation 171\u003c\/p\u003e \u003cp\u003e6.6 The multivariate normal variance–covariance approach 173\u003c\/p\u003e \u003cp\u003e6.7 Non-normal variance–covariance approaches 176\u003c\/p\u003e \u003cp\u003e6.7.1 Multivariate t-distributions 176\u003c\/p\u003e \u003cp\u003e6.7.2 Multivariate elliptical distributions 177\u003c\/p\u003e \u003cp\u003e6.7.3 The Hull–White transformation-into-normality approach 177\u003c\/p\u003e \u003cp\u003e6.8 Handling multivariate return distributions with copulas 178\u003c\/p\u003e \u003cp\u003e6.8.1 Motivation 178\u003c\/p\u003e \u003cp\u003e6.8.2 Estimating VaR with copulas 179\u003c\/p\u003e \u003cp\u003e6.9 Conclusions 182\u003c\/p\u003e \u003cp\u003eAppendix: Forecasting Longer-term Risk Measures 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Parametric Approaches (II): Extreme Value 189\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Generalised extreme-value theory 190\u003c\/p\u003e \u003cp\u003e7.1.1 Theory 190\u003c\/p\u003e \u003cp\u003e7.1.2 A short-cut EV method 194\u003c\/p\u003e \u003cp\u003e7.1.3 Estimation of EV parameters 195\u003c\/p\u003e \u003cp\u003e7.2 The peaks-over-threshold approach: the generalised Pareto distribution 201\u003c\/p\u003e \u003cp\u003e7.2.1 Theory 201\u003c\/p\u003e \u003cp\u003e7.2.2 Estimation 203\u003c\/p\u003e \u003cp\u003e7.2.3 GEV vs POT 204\u003c\/p\u003e \u003cp\u003e7.3 Refinements to EV approaches 204\u003c\/p\u003e \u003cp\u003e7.3.1 Conditional EV 204\u003c\/p\u003e \u003cp\u003e7.3.2 Dealing with dependent (or non-iid) data 205\u003c\/p\u003e \u003cp\u003e7.3.3 Multivariate EVT 206\u003c\/p\u003e \u003cp\u003e7.4 Conclusions 206\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Monte Carlo Simulation Methods 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Uses of Monte carlo simulation 210\u003c\/p\u003e \u003cp\u003e8.2 Monte Carlo simulation with a single risk factor 213\u003c\/p\u003e \u003cp\u003e8.3 Monte Carlo simulation with multiple risk factors 215\u003c\/p\u003e \u003cp\u003e8.4 Variance-reduction methods 217\u003c\/p\u003e \u003cp\u003e8.4.1 Antithetic variables 218\u003c\/p\u003e \u003cp\u003e8.4.2 Control variates 218\u003c\/p\u003e \u003cp\u003e8.4.3 Importance sampling 219\u003c\/p\u003e \u003cp\u003e8.4.4 Stratified sampling 220\u003c\/p\u003e \u003cp\u003e8.4.5 Moment matching 223\u003c\/p\u003e \u003cp\u003e8.5 Advantages and disadvantages of Monte Carlo simulation 225\u003c\/p\u003e \u003cp\u003e8.5.1 Advantages 225\u003c\/p\u003e \u003cp\u003e8.5.2 Disadvantages 225\u003c\/p\u003e \u003cp\u003e8.6 Conclusions 225\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Applications of Stochastic Risk Measurement Methods 227\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Selecting stochastic processes 227\u003c\/p\u003e \u003cp\u003e9.2 Dealing with multivariate stochastic processes 230\u003c\/p\u003e \u003cp\u003e9.2.1 Principal components simulation 230\u003c\/p\u003e \u003cp\u003e9.2.2 Scenario simulation 232\u003c\/p\u003e \u003cp\u003e9.3 Dynamic risks 234\u003c\/p\u003e \u003cp\u003e9.4 Fixed-income risks 236\u003c\/p\u003e \u003cp\u003e9.4.1 Distinctive features of fixed-income problems 237\u003c\/p\u003e \u003cp\u003e9.4.2 Estimating fixed-income risk measures 237\u003c\/p\u003e \u003cp\u003e9.5 Credit-related risks 238\u003c\/p\u003e \u003cp\u003e9.6 Insurance risks 240\u003c\/p\u003e \u003cp\u003e9.6.1 General insurance risks 241\u003c\/p\u003e \u003cp\u003e9.6.2 Life insurance risks 242\u003c\/p\u003e \u003cp\u003e9.7 Measuring pensions risks 244\u003c\/p\u003e \u003cp\u003e9.7.1 Estimating risks of defined-benefit pension plans 245\u003c\/p\u003e \u003cp\u003e9.7.2 Estimating risks of defined-contribution pension plans 246\u003c\/p\u003e \u003cp\u003e9.8 Conclusions 248\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Estimating Options Risk Measures 249\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Analytical and algorithmic solutions for options VaR 249\u003c\/p\u003e \u003cp\u003e10.2 Simulation approaches 253\u003c\/p\u003e \u003cp\u003e10.3 Delta–gamma and related approaches 256\u003c\/p\u003e \u003cp\u003e10.3.1 Delta–normal approaches 257\u003c\/p\u003e \u003cp\u003e10.3.2 Delta–gamma approaches 258\u003c\/p\u003e \u003cp\u003e10.4 Conclusions 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Incremental and Component Risks 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Incremental VaR 265\u003c\/p\u003e \u003cp\u003e11.1.1 Interpreting Incremental VaR 265\u003c\/p\u003e \u003cp\u003e11.1.2 Estimating IVaR by brute force: the ‘before and after’ approach 266\u003c\/p\u003e \u003cp\u003e11.1.3 Estimating IVaR using analytical solutions 267\u003c\/p\u003e \u003cp\u003e11.2 Component VaR 271\u003c\/p\u003e \u003cp\u003e11.2.1 Properties of component VaR 271\u003c\/p\u003e \u003cp\u003e11.2.2 Uses of component VaR 274\u003c\/p\u003e \u003cp\u003e11.3 Decomposition of coherent risk measures 277\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Mapping Positions to Risk Factors 279\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Selecting core instruments 280\u003c\/p\u003e \u003cp\u003e12.2 Mapping positions and VaR estimation 281\u003c\/p\u003e \u003cp\u003e12.2.1 Basic building blocks 281\u003c\/p\u003e \u003cp\u003e12.2.2 More complex positions 287\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Stress Testing 291\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Benefits and difficulties of stress testing 293\u003c\/p\u003e \u003cp\u003e13.1.1 Benefits of stress testing 293\u003c\/p\u003e \u003cp\u003e13.1.2 Difficulties with stress tests 295\u003c\/p\u003e \u003cp\u003e13.2 Scenario analysis 297\u003c\/p\u003e \u003cp\u003e13.2.1 Choosing scenarios 297\u003c\/p\u003e \u003cp\u003e13.2.2 Evaluating the effects of scenarios 300\u003c\/p\u003e \u003cp\u003e13.3 Mechanical stress testing 303\u003c\/p\u003e \u003cp\u003e13.3.1 Factor push analysis 303\u003c\/p\u003e \u003cp\u003e13.3.2 Maximum loss optimisation 305\u003c\/p\u003e \u003cp\u003e13.3.3 CrashMetrics 305\u003c\/p\u003e \u003cp\u003e13.4 Conclusions 306\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Estimating Liquidity Risks 309\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Liquidity and liquidity risks 309\u003c\/p\u003e \u003cp\u003e14.2 Estimating liquidity-adjusted VaR 310\u003c\/p\u003e \u003cp\u003e14.2.1 The constant spread approach 311\u003c\/p\u003e \u003cp\u003e14.2.2 The exogenous spread approach 312\u003c\/p\u003e \u003cp\u003e14.2.3 Endogenous-price approaches 314\u003c\/p\u003e \u003cp\u003e14.2.4 The liquidity discount approach 315\u003c\/p\u003e \u003cp\u003e14.3 Estimating liquidity at risk (LaR) 316\u003c\/p\u003e \u003cp\u003e14.4 Estimating liquidity in crises 319\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Backtesting Market Risk Models 321\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Preliminary data issues 321\u003c\/p\u003e \u003cp\u003e15.2 Backtests based on frequency tests 323\u003c\/p\u003e \u003cp\u003e15.2.1 The basic frequency backtest 324\u003c\/p\u003e \u003cp\u003e15.2.2 The conditional testing (Christoffersen) backtest 329\u003c\/p\u003e \u003cp\u003e15.3 Backtests based on tests of distribution equality 331\u003c\/p\u003e \u003cp\u003e15.3.1 Tests based on the Rosenblatt transformation 331\u003c\/p\u003e \u003cp\u003e15.3.2 Tests using the Berkowitz transformation 333\u003c\/p\u003e \u003cp\u003e15.3.3 Overlapping forecast periods 335\u003c\/p\u003e \u003cp\u003e15.4 Comparing alternative models 336\u003c\/p\u003e \u003cp\u003e15.5 Backtesting with alternative positions and data 339\u003c\/p\u003e \u003cp\u003e15.5.1 Backtesting with alternative positions 340\u003c\/p\u003e \u003cp\u003e15.5.2 Backtesting with alternative data 340\u003c\/p\u003e \u003cp\u003e15.6 Assessing the precision of backtest results 340\u003c\/p\u003e \u003cp\u003e15.7 Summary and conclusions 342\u003c\/p\u003e \u003cp\u003eAppendix: Testing Whether Two Distributions are Different 343\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Model Risk 351\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Models and model risk 351\u003c\/p\u003e \u003cp\u003e16.2 Sources of model risk 353\u003c\/p\u003e \u003cp\u003e16.2.1 Incorrect model specification 353\u003c\/p\u003e \u003cp\u003e16.2.2 Incorrect model application 354\u003c\/p\u003e \u003cp\u003e16.2.3 Implementation risk 354\u003c\/p\u003e \u003cp\u003e16.2.4 Other sources of model risk 355\u003c\/p\u003e \u003cp\u003e16.3 Quantifying model risk 357\u003c\/p\u003e \u003cp\u003e16.4 Managing model risk 359\u003c\/p\u003e \u003cp\u003e16.4.1 Managing model risk: some guidelines for risk practitioners 359\u003c\/p\u003e \u003cp\u003e16.4.2 Managing model risk: some guidelines for senior managers 360\u003c\/p\u003e \u003cp\u003e16.4.3 Institutional methods to manage model risk 361\u003c\/p\u003e \u003cp\u003e16.5 Conclusions 363\u003c\/p\u003e \u003cp\u003eBibliography 365\u003c\/p\u003e \u003cp\u003eIndex 379\u003c\/p\u003e \u003cb\u003eKevin Dowd\u003c\/b\u003e is Professor of Financial Risk Management at Nottingham University. Kevin is an Adjunct Scholar at the Cato Institute in Washington, D.C., and a Fellow of the Pensions Institute at Birkbeck College.  The second edition of \u003ci\u003eMeasuring Market Risk\u003c\/i\u003e provides an extensive treatment of the state of the art in market risk measurement. The book covers all aspects of modern market risk measurement, and in doing so emphasises new developments in the subject such as coherent and spectral risk measures, the uses of copulas, new applications of stochastic methods, and new developments in backtesting.  \u003cp\u003eThe topics covered include: the rise of VaR as a risk measure; different measures of financial risk (including coherent and distortion risk measures); non-parametric approaches (including the bootstrap, order statistics, non-parametric density estimation, and principal components and factor analysis); parametric approaches (including copulas and extreme-value approaches); the theory and applications of stochastic methods; the forecasting of volatilities and correlations; liquidity risk; options risk measurement; risk decomposition; mapping; stress-testing; backtesting; and model risk.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMeasuring Market Risk\u003c\/i\u003e is written in a clear and accessible style, and includes many worked examples of market risk measurement problems.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989594095845,"sku":"NP9780470013038","price":131.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470013038.jpg?v=1761784735","url":"https:\/\/k12savings.com\/products\/measuring-market-risk-isbn-9780470013038","provider":"K12savings","version":"1.0","type":"link"}