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The Analytics of Risk Model Validation

Book Description

Risk model validation is an emerging and important area of research, and has arisen because of Basel I and II. These regulatory initiatives require trading institutions and lending institutions to compute their reserve capital in a highly analytic way, based on the use of internal risk models. It is part of the regulatory structure that these risk models be validated both internally and externally, and there is a great shortage of information as to best practise. Editors Christodoulakis and Satchell collect papers that are beginning to appear by regulators, consultants, and academics, to provide the first collection that focuses on the quantitative side of model validation. The book covers the three main areas of risk: Credit Risk and Market and Operational Risk.

*Risk model validation is a requirement of Basel I and II
*The first collection of papers in this new and developing area of research
*International authors cover model validation in credit, market, and operational risk

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright page
  5. About the editors
  6. About the contributors
  7. Preface
  8. 1: Determinants of small business default*
    1. Abstract
    2. 1 Introduction
    3. 2 Data, methodology and summary statistics
    4. 3 Empirical results of small business default
    5. 4 Conclusion
  9. 2: Validation of stress testing models
    1. Abstract
    2. 1 Why stress test?
    3. 2 Stress testing basics
    4. 3 Overview of validation approaches
    5. 4 Subsampling tests
    6. 5 Ideal scenario validation
    7. 6 Scenario validation
    8. 7 Cross-segment validation
    9. 8 Back-casting
    10. 9 Conclusions
  10. 3: The validity of credit risk model validation methods*
    1. Abstract
    2. 1 Introduction
    3. 2 Measures of discriminatory power
    4. 3 Uncertainty in credit risk model validation
    5. 4 Confidence interval for ROC
    6. 5 Bootstrapping
    7. 6 Optimal rating combinations
    8. 7 Concluding remarks
  11. 4: A moments-based procedure for evaluating risk forecasting models
    1. Abstract
    2. 1 Introduction
    3. 2 Preliminary analysis
    4. 3 The likelihood ratio test
    5. 4 A moments test of model adequacy
    6. 5 An illustration
    7. 6 Conclusions
    8. 7 Acknowledgements
    9. Appendix
    10. 1 Error distribution
    11. 2 Two-piece normal distribution
    12. 3 <span xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" class="italic">t</span>-Distribution-Distribution
    13. 4 Skew-<span xmlns="http://www.w3.org/1999/xhtml" xmlns:epub="http://www.idpf.org/2007/ops" class="italic">t</span> distribution distribution
  12. 5: Measuring concentration risk in credit portfolios
    1. Abstract
    2. 1 Concentration risk and validation
    3. 2 Concentration risk and the IRB model
    4. 3 Measuring name concentration
    5. 4 Measuring sectoral concentration
    6. 5 Numerical example
    7. 6 Future challenges of concentration risk measurement
    8. 7 Summary
    9. Appendix A.1 IRB risk weight functions and concentration risk
    10. Appendix A.2 Factor surface for the diversification factor
    11. Appendix A.3
  13. 6: A Simple method for regulators to cross-check operational risk loss models for banks
    1. Abstract
    2. 1 Introduction
    3. 2 Background
    4. 3 Cross-checking procedure
    5. 4 Justification of our approach
    6. 5 Justification for a lower bound using the lognormal distribution
    7. 6 Conclusion
  14. 7: Of the credibility of mapping and benchmarking credit risk estimates for internal rating systems
    1. Abstract
    2. 1 Introduction
    3. 2 Why does the portfolio’s structure matter?
    4. 3 Credible credit ratings and credible credit risk estimates
    5. 4 An empirical illustration
    6. 5 Credible mapping
    7. 6 Conclusions
    8. 7 Acknowledgements
    9. Appendix
  15. 8: Analytic models of the ROC Curve: Applications to credit rating model validation
    1. Abstract
    2. 1 Introduction
    3. 2 Theoretical implications and applications
    4. 3 Choices of distributions
    5. 4 Performance evaluation on the AUROC estimation with simulated data
    6. 5 Summary
    7. 6 Conclusions
    8. 7 Acknowledgements
    9. Appendix
  16. 9: The validation of equity portfolio risk models
    1. Abstract
    2. 1 Linear factor models
    3. 2 Building a time series model
    4. 3 Building a statistical factor model
    5. 4 Building models with known beta’s
    6. 5 Forecast construction and evaluation
    7. 6 Diagnostics
    8. 7 Time horizons and data frequency
    9. 8 The residuals
    10. 9 Monte Carlo procedures
    11. 10 Conclusions
  17. 10: Dynamic risk analysis and risk model evaluation
    1. Abstract
    2. 1 Introduction
    3. 2 Volatility over time and the cumulative variance
    4. 3 Beta over time and cumulative covariance
    5. 4 Dynamic risk model evaluation
    6. 5 Summary
  18. 11: Validation of internal rating systems and PD estimates
    1. Abstract
    2. 1 Introduction
    3. 2 Regulatory background
    4. 3 Statistical background
    5. 4 Monotonicity of conditional PDs
    6. 5 Discriminatory power of rating systems
    7. 6 Calibration of rating systems
    8. 7 Conclusions
  19. Index