Measuring Market Risk – Kevin Dowd

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Description

Fully revised and restructured, Measuring Market Risk, Second Edition 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&A’s and case studies.

Table of Contents

Preface to the Second Edition

Acknowledgments

• 1 The Rise of Value at Risk
  • 1.1 The emergence of financial risk management
  • 1.2 Market risk management
  • 1.3 Risk management before VaR
  • 1.4 Value at risk

Appendix 1: Types of Market Risk

• 2 Measures of Financial Risk
  • 2.1 The Mean-Variance framework for measuring financial risk
  • 2.2 Value at risk
  • 2.3 Coherent risk measures
  • 2.4 Conclusions

Appendix 1: Probability Functions

Appendix 2: Regulatory Uses of VaR

• 3 Estimating Market Risk Measures: An Introduction and Overview
  • 3.1 Data
  • 3.2 Estimating historical simulation VaR
  • 3.3 Estimating parametric VaR
  • 3.4 Estimating coherent risk measures
  • 3.5 Estimating the standard errors of risk measure estimators
  • 3.6 Overview

Appendix 1: Preliminary Data Analysis

Appendix 2: Numerical Integration Methods

• 4 Non-parametric Approaches
  • 4.1 Compiling historical simulation data
  • 4.2 Estimation of historical simulation VaR and ES
  • 4.3 Estimating confidence intervals for historical simulation VaR and ES
  • 4.4 Weighted historical simulation
  • 4.5 Advantages and disadvantages of non-parametric methods
  • 4.6 Conclusions

Appendix 1: Estimating Risk Measures with Order Statistics

Appendix 2: The Bootstrap

Appendix 3: Non-parametric Density Estimation

Appendix 4: Principal Components Analysis and Factor Analysis

• 5 Forecasting Volatilities, Covariances, and Correlations
  • 5.1 Forecasting volatilities
  • 5.2 Forecasting covariances and correlations
  • 5.3 Forecasting covariance matrices

Appendix 1: Modelling Dependence: Correlations and Copulas

• 6 Parametric Approaches (I)
  • 6.1 Conditional vs unconditional distributions
  • 6.2 Normal VaR and ES
  • 6.3 The t-distribution
  • 6.4 The lognormal distribution
  • 6.5 Miscellaneous parametric approaches
  • 6.6 The multivariate normal variance-covariance approach
  • 6.7 Non-normal variance–covariance approaches
  • 6.8 Handling multivariate return distributions with copulas
  • 6.9 Conclusions

Appendix 1: Forecasting longer-term Risk Measures

• 7 Parametric Approaches (II): Extreme Value
  • 7.1 Generalised extreme-value theory
  • 7.2 The peaks-over-threshold approach: the generalized Pareto distribution
  • 7.3 Refinements to EV approaches
  • 7.4 Conclusions
• 8 Monte Carlo Simulation Methods
  • 8.1 Uses of monte CarloCarlo simulation
  • 8.2 Monte carlo simulation with a single risk factor
  • 8.3 Monte Carlo simulation with multiple risk factors
  • 8.4 Variance-reduction methods
  • 8.5 Advantages and disadvantages of Monte Carlo simulation
  • 8.6 Conclusions
• 9 Applications of Stochastic Risk Measurement Methods
  • 9.1 Selecting stochastic processes
  • 9.2 Dealing with multivariate stochastic processes
  • 9.3 Dynamic risks
  • 9.4 Fixed-income risks
  • 9.5 Credit-related risks
  • 9.6 Insurance risks
  • 9.7 Measuring pensions risks
  • 9.8 Conclusions
• 10 Estimating Options Risk Measures
  • 10.1 Analytical and algorithmic solutions m for options VaR
  • 10.2 Simulation approaches
  • 10.3 Delta–gamma and related approaches
  • 10.4 Conclusions
• 11 Incremental and Component Risks
  • 11.1 Incremental VaR
  • 11.2 Component VaR
  • 11.3 Decomposition of coherent risk measures
• 12 Mapping Positions to Risk Factors
  • 12.1 Selecting core instruments
  • 12.2 Mapping positions and VaR estimation
• 13 Stress Testing
  • 13.1 Benefits and difficulties of stress testing
  • 13.2 Scenario analysis
  • 13.3 Mechanical stress testing
  • 13.4 Conclusions
• 14 Estimating Liquidity Risks
  • 14.1 Liquidity and liquidity risks
  • 14.2 Estimating liquidity-adjusted VaR
  • 14.3 Estimating liquidity at risk (LaR)
  • 14.4 Estimating liquidity in crises
• 15 Backtesting Market Risk Models
  • 15.1 Preliminary data issues
  • 15.2 Backtests based on frequency tests
  • 15.3 Backtests based on tests of distribution equality
  • 15.4 Comparing alternative models
  • 15.5 Backtesting with alternative positions and data
  • 15.6 Assessing the precision of backtest results
  • 15.7 Summary and conclusions

Appendix 1: Testing Whether Two Distributions are Different

• 16 Model Risk
  • 16.1 Models and model risk
  • 16.2 Sources of model risk
  • 16.3 Quantifying model risk
  • 16.4 Managing model risk
  • 16.5 Conclusions

Bibliography

Author Index

Subject Index