Description 
1 online resource (xvii, 225 pages) : illustrations 
Series 
EAA lecture notes, 18652174 

EAA lecture notes.

Contents 
Introduction to Credit Risk Modeling 1 Risk Measurement 3 1.1 Variables of Risk 4 1.2 The General Model Setting 5 1.3 Exchangeable Models 7 2 Modeling Credit Risk 9 2.1 The Regulatory Framework 10 2.2 Expected and Unexpected Loss 12 2.3 ValueatRisk 13 2.4 Expected Shortfall 15 2.5 Economic Capital 17 3 The Merton Model 19 3.1 The General Framework 20 3.2 The MultiFactor Merton Model 23 3.3 Industry Models Based on the Merton Approach 29 3.3.1 The KMV Model 29 3.3.2 The CreditMetrics Model 30 4 The Asymptotic Single Risk Factor Model 31 4.1 The ASRF Model 32 4.2 The IRB Risk Weight Functions 35 4.3 The Loss Distribution of an Infinitely Granular Portfolio 38 5 Mixture Models 43 5.1 Bernoulli and Poisson Mixture Models 43 5.2 The Influence of the Mixing Distribution on the Loss Distribution 48 5.3 Relation Between Latent Variable Models and Mixture Models 50 6 The CreditRisk[superscript +] Model 53 6.1 Basic Model Setting 54 6.2 The Poisson Approximation 56 6.3 Model with Random Default Probabilities 57 Part II Concentration Risk in Credit Portfolios 7 Introduction 63 8 AdHoc Measures of Concentration 67 8.1 Concentration Indices 68 8.2 Conclusion 72 9 Name Concentration 75 9.1 A Granularity Adjustment for the ASRF Model 76 9.1.1 Example as Motivation for GA Methodology 77 9.1.2 The General Framework 78 9.1.3 The Granularity Adjustment in a Single Factor CreditRisk[superscript +] Setting 81 9.1.4 Data on German Bank Portfolios 84 9.1.5 Numerical Results 86 9.1.6 Summary 88 9.2 The SemiAsymptotic Approach 90 9.2.1 The General Framework 90 9.2.2 Numerical Results 93 9.3 Methods Based on the SaddlePoint Approximation 93 9.3.1 The General Framework 94 9.3.2 Application to Name Concentration Risk 96 9.4 Discussion and Comparison Study of the Granularity Adjustment Methods 99 9.4.1 Empirical Relevance of the Granularity Adjustment 100 9.4.2 Why a Granularity Adjustment Instead of the HHI? 100 9.4.3 Accuracy of the Granularity Adjustment and Robustness to Regulatory Parameters 102 9.4.4 Comparison of Granularity Adjustment with Other ModelBased Approaches 103 9.4.5 Agreement of Granularity Adjustment and SaddlePoint Approximation Method in the CreditRisk[superscript +] Model 104 10 Sector Concentration 107 10.1 Analytical Approximation Models 108 10.1.1 Analytical Approximation for ValueatRisk 109 10.1.2 Analytical Approximation for Expected Shortfall 117 10.1.3 Performance Testing 118 10.1.4 Summary and Discussion 119 10.2 Diversification Factor Models 120 10.2.1 The MultiSector Framework 121 10.2.2 The Capital Diversification Factor 123 10.2.3 Marginal Capital Contributions 124 10.2.4 Parameterization 126 10.2.5 Application to a Bank Internal MultiFactor Model 127 10.2.6 Discussion 129 11 Empirical Studies on Concentration Risk 131 11.1 Sector Concentration and Economic Capital 132 11.1.1 The Model Framework 133 11.1.2 Data Description and Portfolio Composition 133 11.1.3 Impact of Sector Concentration on Economic Capital 135 11.1.4 Robustness of EC Approximations 136 11.1.5 Discussion 139 11.2 The Influence of Systematic and Idiosyncratic Risk on Large Portfolio Losses 140 11.2.1 Descriptive Analysis of SNC Data 140 11.2.2 Simple Indices of Name and Sector Concentration 141 11.2.3 Modeling Dependencies in Losses 142 11.2.4 Monte Carlo Simulation of the Portfolio Loss Distribution 143 11.2.5 Empirical Results 145 11.2.6 Summary and Discussion 147 Part III Default Contagion 12 Introduction 151 13 Empirical Studies on Default Contagion 155 13.1 The Doubly Stochastic Property and its Testable Implications 156 13.2 Data for Default Intensity Estimates 159 13.3 GoodnessofFit Tests 159 13.4 Discussion 162 14 Models Based on Copulas 165 14.1 Equivalence of Latent Variable Models 166 14.2 Sensitivity of Losses on the Dependence Structure 168 14.3 Discussion 170 15 A Voter Model for Credit Contagion 173 15.1 The Model Framework 174 15.2 Invariant and Ergodic Measures for the Voter Model 177 15.3 The NonDense Business Partner Network 179 15.4 The Dense Business Partner Network 180 15.5 Aggregate Losses on Large Portfolios 182 15.6 Discussion and Comparison with Alternative Approaches 186 15.6.1 The MeanField Model with Interacting Default Intensities 187 15.6.2 A Dynamic Contagion Model 189 15.7 Contagion Through Macro and Microstructural Channels 190 15.7.1 A Model with Macro and MicroStructural Dependence 191 15.7.2 The Rating Migrations Process 193 15.7.3 Results and Discussion 194 16 Equilibrium Models 197 16.1 A MeanField Model of Credit Ratings 198 16.2 The MeanField Model with Local Interactions 202 16.3 Large Portfolio Losses 205 16.4 Discussion 
Summary 
Modeling and management of credit risk are the main topics within banks and other lending institutions. Historical experience shows that, in particular, concentration of risk in credit portfolios has been one of the major causes of bank distress. Therefore, concentration risk is highly relevant to anyone who wants to go beyond the very basic portfolio credit risk models. The book gives an introduction to credit risk modeling with the aim to measure concentration risks in credit portfolios. Taking the basic principles of credit risk in general as a starting point, several industry models are studied. These allow banks to compute a probability distribution of credit losses at the portfolio level. Besides these industry models the Internal Ratings Based model, on which Basel II is based, is treated. On the basis of these models various methods for the quantification of name and sector concentration risk and the treatment of default contagion are discussed. The book reflects current research in these areas from both an academic and a supervisory perspective 
Bibliography 
Includes bibliographical references (pages 217222) and index 
Notes 
Print version record 
Subject 
Credit  Management.


Credit  Methodology.


Credit scoring systems  Evaluation.


Portfolio management.


Risk management.

Form 
Electronic book

LC no. 
2008936503 
ISBN 
3540708693 (paperback) 

3540708707 (eisbn) 

9783540708698 (paperback) 

9783540708704 (eisbn) 
