Click on one of bellow shared links to download

---

By Bra... on Jan 25, 2021 View Files Download

Share Your Link

Kevin Dowd - Measuring Market Risk

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

Author Information

Kevin Dowd is a 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.

Get Measuring Market Risk or the other courses from the same one of these categories: Kevin Dowd, Trading, eBook for free on Course Sharing.

Share Course Measuring Market Risk, Free Download Measuring Market Risk, Measuring Market Risk Torrent, Measuring Market Risk Download Free, Measuring Market Risk Discount, Measuring Market Risk Review, Kevin Dowd – Measuring Market Risk, Measuring Market Risk, Kevin Dowd.