LEADER 05561nam 2200697 a 450 001 9910784746303321 005 20230620232028.0 010 $a1-281-07150-1 010 $a9786611071509 010 $a0-08-055388-5 035 $a(CKB)1000000000404420 035 $a(EBL)319164 035 $a(OCoLC)476115061 035 $a(SSID)ssj0000103109 035 $a(PQKBManifestationID)11990899 035 $a(PQKBTitleCode)TC0000103109 035 $a(PQKBWorkID)10060978 035 $a(PQKB)11736561 035 $a(Au-PeEL)EBL319164 035 $a(CaPaEBR)ebr10206015 035 $a(CaONFJC)MIL107150 035 $a(OCoLC)228147999 035 $a(CaSebORM)9780750681582 035 $a(MiAaPQ)EBC319164 035 $a(PPN)170234851 035 $a(EXLCZ)991000000000404420 100 $a20080816d2008 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 04$aThe analytics of risk model validation$b[electronic resource] /$fedited by George Christodoulakis, Stephen Satchell 205 $a1st edition 210 $aAmsterdam $cAcademic Press$d2008 215 $a1 online resource (217 p.) 225 1 $aQuantitative finance series 300 $aDescription based upon print version of record. 311 $a0-7506-8158-6 320 $aIncludes bibliographical references and index. 327 $aFront 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 327 $a8. Back-casting 9. 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 327 $aNotes Appendix; 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.3 327 $aChapter 6 A simple method for regulators to cross-check operational risk loss models for banks Abstract; 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 327 $a6. Conclusions 7. 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 Bu?hlmann 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 327 $a5. Summary 330 $aRisk model validation is an emerging and important area of research, and has arisen because of Basel I and II. These regulatory initiatives require trading institutions and lending institutions to compute their reserve capital in a highly analytic way, based on the use of internal risk models. It is part of the regulatory structure that these risk models be validated both internally and externally, and there is a great shortage of information as to best practise. Editors Christodoulakis and Satchell collect papers that are beginning to appear by regulators, consultants, and academics, 410 0$aQuantitative finance series. 606 $aRisk management$xMathematical models 615 0$aRisk management$xMathematical models. 676 $a336.3 676 $a658.155015118 701 $aChristodoulakis$b George$01571115 701 $aSatchell$b Stephen$f1949-$01364640 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910784746303321 996 $aThe analytics of risk model validation$93845288 997 $aUNINA