Kevin Dowd – Measuring Market Risk
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Kevin Dowd – Measuring Market Risk
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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.
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