Credit Risk Modeling using Excel and VBA
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This book provides practitioners and students with a hands-on introduction to modern credit risk modeling. The authors begin each chapter with an accessible presentation of a given methodology, before providing a step-by-step guide to implementation methods in Excel and Visual Basic for Applications (VBA). The book covers default probability estimation (scoring, structural models, and transition matrices), correlation and portfolio analysis, validation, as well as credit default swaps and structured finance. Several appendices and videos increase ease of access.The second edition includes new ...
This book provides practitioners and students with a hands-on introduction to modern credit risk modeling. The authors begin each chapter with an accessible presentation of a given methodology, before providing a step-by-step guide to implementation methods in Excel and Visual Basic for Applications (VBA). The book covers default probability estimation (scoring, structural models, and transition matrices), correlation and portfolio analysis, validation, as well as credit default swaps and structured finance. Several appendices and videos increase ease of access.
The second edition includes new coverage of the important issue of how parameter uncertainty can be dealt with in the estimation of portfolio risk, as well as comprehensive new sections on the pricing of CDSs and CDOs, and a chapter on predicting borrower-specific loss given default with regression models. In all, the authors present a host of applications - many of which go beyond standard Excel or VBA usages, for example, how to estimate logit models with maximum likelihood, or how to quickly conduct large-scale Monte Carlo simulations.
Contents
Preface
Some hints for troubleshooting
1. Estimating credit scores with Logit
2. The structural approach to default prediction and valuation
3. Transition matrices
4. Prediction of default and transition rates
5. Prediction of loss given default
6. Modeling and estimating default correlations with the asset value approach
7. Measuring credit portfolio risk with the asset value approach
8. Validation of rating systems
9. Validation of credit portfolio models
10. Risk-neutral default probabilities and credit default swaps
11. Risk analysis and pricing of structured credit: CDOs and first-to-default swaps
12. Basel II and internal ratings
Appendix 1 VBA
Appendix 2 Solver
Appendix 3 Maximum Likelihood
Appendix 4 Testing and goodness of fit
Appendix 5 List of user-defined Functions
The second edition includes new coverage of the important issue of how parameter uncertainty can be dealt with in the estimation of portfolio risk, as well as comprehensive new sections on the pricing of CDSs and CDOs, and a chapter on predicting borrower-specific loss given default with regression models. In all, the authors present a host of applications - many of which go beyond standard Excel or VBA usages, for example, how to estimate logit models with maximum likelihood, or how to quickly conduct large-scale Monte Carlo simulations.
Contents
Preface
Some hints for troubleshooting
1. Estimating credit scores with Logit
2. The structural approach to default prediction and valuation
3. Transition matrices
4. Prediction of default and transition rates
5. Prediction of loss given default
6. Modeling and estimating default correlations with the asset value approach
7. Measuring credit portfolio risk with the asset value approach
8. Validation of rating systems
9. Validation of credit portfolio models
10. Risk-neutral default probabilities and credit default swaps
11. Risk analysis and pricing of structured credit: CDOs and first-to-default swaps
12. Basel II and internal ratings
Appendix 1 VBA
Appendix 2 Solver
Appendix 3 Maximum Likelihood
Appendix 4 Testing and goodness of fit
Appendix 5 List of user-defined Functions