Kevin Dowd

Measuring Market Risk (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 410
• Erscheinungstermin: 11. Januar 2007
• Englisch
• ISBN-13: 9780470016510
• Artikelnr.: 37289865

Autorenporträt

Kevin Dowd 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.

Inhaltsangabe

Preface to the Second Edition Acknowledgements 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 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 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