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The analytics of risk model validation /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam ; Boston :
Elsevier/Academic Press,
2008.
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| Edición: | 1st ed. |
| Colección: | Elsevier finance.
Quantitative finance series. |
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
Tabla de Contenidos:
- Front Cover; The Analytics of Risk Model Validation; Copyright Page; Table of Contents; About the editors; About the contributors; Preface; Chapter 1 Determinants of small business default; Abstract; 1. Introduction; 2. Data, methodology and summary statistics; 3. Empirical results of small business default; 4. Conclusion; References; Notes; Chapter 2 Validation of stress testing models; Abstract; 1. Why stress test?; 2. Stress testing basics; 3. Overview of validation approaches; 4. Subsampling tests; 5. Ideal scenario validation; 6. Scenario validation; 7. Cross-segment validation.
- 8. Back-casting9. Conclusions; References; Chapter 3 The validity of credit risk model validation methods; Abstract; 1. Introduction; 2. Measures of discriminatory power; 3. Uncertainty in credit risk model validation; 4. Confidence interval for ROC; 5. Bootstrapping; 6. Optimal rating combinations; 7. Concluding remarks; References; Chapter 4 A moments-based procedure for evaluating risk forecasting models; Abstract; 1. Introduction; 2. Preliminary analysis; 3. The likelihood ratio test; 4. A moments test of model adequacy; 5. An illustration; 6. Conclusions; 7. Acknowledgements; References.
- NotesAppendix; 1. Error distribution; 2. Two-piece normal distribution; 3. t-Distribution; 4. Skew-t distribution; Chapter 5 Measuring concentration risk in credit portfolios; Abstract; 1. Concentration risk and validation; 2. Concentration risk and the IRB model; 3. Measuring name concentration; 4. Measuring sectoral concentration; 5. Numerical example; 6. Future challenges of concentration risk measurement; 7. Summary; References; Notes; Appendix A.1: IRB risk weight functions and concentration risk; Appendix A.2: Factor surface for the diversification factor; Appendix A.3.
- Chapter 6 A simple method for regulators to cross-check operational risk loss models for banksAbstract; 1. Introduction; 2. Background; 3. Cross-checking procedure; 4. Justification of our approach; 5. Justification for a lower bound using the lognormal distribution; 6. Conclusion; References; Chapter 7 Of the credibility of mapping and benchmarking credit risk estimates for internal rating systems; Abstract; 1. Introduction; 2. Why does the portfolio's structure matter?; 3. Credible credit ratings and credible credit risk estimates; 4. An empirical illustration; 5. Credible mapping.
- 6. Conclusions7. Acknowledgements; References; Appendix; 1. Further elements of modern credibility theory; 2. Proof of the credibility fundamental relation; 3. Mixed Gamma-Poisson distribution and negative binomial; 4. Calculation of the Bühlmann credibility estimate under the Gamma-Poisson model; 5. Calculation of accuracy ratio; Chapter 8 Analytic models of the ROC curve: Applications to credit rating model validation; Abstract; 1. Introduction; 2. Theoretical implications and applications; 3. Choices of distributions; 4. Performance evaluation on the AUROC estimation with simulated data.