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The analytics of risk model validation [electronic resource]
להגדלת הטקסט להקטנת הטקסט- ספר
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,
| כותר |
The analytics of risk model validation [electronic resource] / edited by George Christodoulakis, Stephen Satchell. |
|---|---|
| מהדורה |
1st edition. |
| מוציא לאור |
Amsterdam : Academic Press |
| שנה |
2008 |
| הערות |
Description based upon print version of record. Includes bibliographical references and index. English |
| הערת תוכן ותקציר |
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 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-casting 9. Conclusions Chapter 3 The validity of credit risk model validation methods 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 Chapter 4 A moments-based procedure for evaluating risk forecasting models 2. Preliminary analysis 3. The likelihood ratio test 4. A moments test of model adequacy 5. An illustration 6. Conclusions 7. Acknowledgements Notes Appendix 1. Error distribution 2. Two-piece normal distribution 3. t-Distribution 4. Skew-t distribution Chapter 5 Measuring concentration risk in credit portfolios 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 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 banks Abstract 2. Background 3. Cross-checking procedure 4. Justification of our approach 5. Justification for a lower bound using the lognormal distribution 6. Conclusion Chapter 7 Of the credibility of mapping and benchmarking credit risk estimates for internal rating systems 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. Conclusions 7. Acknowledgements 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 2. Theoretical implications and applications 3. Choices of distributions 4. Performance evaluation on the AUROC estimation with simulated data 5. Summary |
| סדרה |
Quantitative finance series |
| היקף החומר |
1 online resource (217 p.) |
| שפה |
אנגלית |
| מספר מערכת |
997010710621505171 |
תצוגת MARC
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