Kevin Dowd – Measuring Market Risk

Description

Kevin Dowd – Measuring Market Risk

Fully revised and restructured, Measuring Market Risk, Second Edition features a new chapter on choices danger administration, in addition to substantial new data on parametric danger, non-parametric measurements and liquidity dangers, extra sensible data to assist with particular calculations, and new examples together with Q&A’s and case research.

Table of Contents

Preface to the Second Edition

Acknowledgements

1 The Rise of Value at Risk

1.1 The emergence of monetary danger administration

1.2 Market danger administration

1.3 Risk administration earlier than VaR

1.4 Value in danger

Appendix 1: Types of Market Risk

2 Measures of Financial Risk

2.1 The Mean–Variance framework for measuring monetary danger

2.2 Value in danger

2.3 Coherent danger 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 historic simulation VaR

3.3 Estimating parametric VaR

3.4 Estimating coherent danger measures

3.5 Estimating the usual errors of danger measure estimators

3.6 Overview

Appendix 1: Preliminary Data Analysis

Appendix 2: Numerical Integration Methods

4 Non-parametric Approaches

4.1 Compiling historic simulation information

4.2 Estimation of historic simulation VaR and ES

4.3 Estimating confidence intervals for historic simulation VaR and ES

4.4 Weighted historic simulation

4.5 Advantages and downsides of non-parametric strategies

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 regular variance–covariance strategy

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 idea

7.2 The peaks-over-threshold strategy: the generalised pareto distribution

7.3 Refinements to EV approaches

7.4 Conclusions

8 Monte Carlo Simulation Methods

8.1 Uses of monte carlo simulation

8.2 Monte carlo simulation with a single danger issue

8.3 Monte carlo simulation with a number of danger elements

8.4 Variance-reduction strategies

8.5 Advantages and downsides 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 dangers

9.4 Fixed-income dangers

9.5 Credit-related dangers

9.6 Insurance dangers

9.7 Measuring pensions dangers

9.8 Conclusions

10 Estimating Options Risk Measures

10.1 Analytical and algorithmic options m for choices VaR

10.2 Simulation approaches

10.3 Delta–gamma and associated approaches

10.4 Conclusions

11 Incremental and Component Risks

11.1 Incremental VaR

11.2 Component VaR

11.3 Decomposition of coherent danger measures

12 Mapping Positions to Risk Factors

12.1 Selecting core devices

12.2 Mapping positions and VaR estimation

13 Stress Testing

13.1 Benefits and difficulties of stress testing

13.2 Scenario evaluation

13.3 Mechanical stress testing

13.4 Conclusions

14 Estimating Liquidity Risks

14.1 Liquidity and liquidity dangers

14.2 Estimating liquidity-adjusted VaR

14.3 Estimating liquidity in danger (LaR)

14.4 Estimating liquidity in crises

15 Backtesting Market Risk Models

15.1 Preliminary information points

15.2 Backtests primarily based on frequency checks

15.3 Backtests primarily based on checks of distribution equality

15.4 Comparing different fashions

15.5 Backtesting with different positions and information

15.6 Assessing the precision of backtest outcomes

15.7 Summary and conclusions

Appendix 1: Testing Whether Two Distributions are Different

16 Model Risk

16.1 Models and mannequin danger

16.2 Sources of mannequin danger

16.3 Quantifying mannequin danger

16.4 Managing mannequin danger

16.5 Conclusions

Bibliography

Author Index

Subject Index

 

Author Information

Kevin Dowd is Professor of Financial Risk Management at Nottingham University. Kevin is an Adjunct Scholar on the Cato Institute in Washington, D.C., and a Fellow of the Pensions Institute at Birkbeck College.