Credit Risk Modeling Using Excel and VBA
2nd edition
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- Nombre de pages342
- PrésentationRelié
- FormatGrand Format
- Poids0.8 kg
- Dimensions17,5 cm × 25,0 cm × 2,2 cm
- ISBN978-0-470-66092-8
- EAN9780470660928
- Date de parution01/01/2011
- ÉditeurWiley
Résumé
This book provides practitioners and students with a hands-on introduction to modem 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). Th…
This book provides practitioners and students with a hands-on introduction to modem 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.
Clearly written with a multitude of practical examples, the new edition of Credit Risk Modeling using Excel and VBA will prove an indispensable resource for anyone working in, studying or researching this important field.
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.
Clearly written with a multitude of practical examples, the new edition of Credit Risk Modeling using Excel and VBA will prove an indispensable resource for anyone working in, studying or researching this important field.