In today's increasingly competitive financial world, successful
risk management, portfolio management, and financial structuring
demand more than up-to-date financial know-how. They also call for
quantitative expertise, including the ability to effectively apply
mathematical modeling tools and techniques, in this case credit.
Credit Risk Modeling using Excel and VBA with DVD
provides practitioners with a hands on introduction to credit risk
modeling. Instead of just presenting analytical methods it
shows how to implement them using Excel and VBA, in addition to a
detailed description in the text a DVD guides readers step by step
through the implementation. The authors begin by showing how
to use option theoretic and statistical models to estimate a
borrowers default risk. The second half of the book is
devoted to credit portfolio risk. The authors guide readers
through the implementation of a credit risk model, show how
portfolio models can be validated or used to access structured
credit products like CDO's. The final chapters address
modeling issues associated with the new Basel Accord.
Autorentext
GUNTER LÖFFLER is professor of finance at the University of Ulm in Germany. His current research interests are on credit risk and empirical finance. Previously, Gunter was assistant professor at Goethe University Frankfurt, and served as an internal consultant in the asset management division of Commerzbank. His Ph.D. in finance is from the University of Mannheim. Gunter has studied at Heidelberg and Cambridge Universities.
PETER N. POSCH is PhD student in finance at the chair of Gunter Löffler. His current research focus is on credit risk and financial econometrics. Peter studied philosophy and economics and holds a Diplom, M.Sc. equivalent, in economics from the University of Bonn.
Klappentext
This book provides practitioners and students with an intuitive, hands-on introduction to modern credit risk modeling. A typical chapter starts with an approachable presentation of the methodology. Step by step, the authors then show how to implement the methods in Excel and Visual Basic for Applications. Focusing on risk management issues, 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 authors present a host of applications many of which go beyond standard Excel or VBA usages. For example, they show how to estimate logit models with maximum likelihood, or how to conduct large-scale Monte Carlo simulations in little time. Even to experienced modelers the book can serve as a toolbox and source of inspiration.
"In one place, Löffler and Posch provide all that is needed to install state-of-the-art risk management system, including a broad understanding of different risk management frameworks, detailed estimation techniques for deriving PD, LGD, and correlation parameters, and programing tools for putting these methods into practice."
Richard Cantor, Managing Director, Credit Policy Research, Moody's Investors Service
"I read this book cover-to-cover and recommend it heartily. For each topic, there is straightforward explanation, practical examples, and implementable coding. This book would have saved me months of effort many times over with its full 'toolset' of Excel/VBA code. I have immediate plans to reread sections and incorporate sections of code into my own spreadsheets."
Greg M. Gupton, Fitch Ratings & DefaultRisk.com
Zusammenfassung
In today's increasingly competitive financial world, successful risk management, portfolio management, and financial structuring demand more than up-to-date financial know-how. They also call for quantitative expertise, including the ability to effectively apply mathematical modeling tools and techniques, in this case credit.
Credit Risk Modeling using Excel and VBA with DVD provides practitioners with a hands on introduction to credit risk modeling. Instead of just presenting analytical methods it shows how to implement them using Excel and VBA, in addition to a detailed description in the text a DVD guides readers step by step through the implementation. The authors begin by showing how to use option theoretic and statistical models to estimate a borrowers default risk. The second half of the book is devoted to credit portfolio risk. The authors guide readers through the implementation of a credit risk model, show how portfolio models can be validated or used to access structured credit products like CDO's. The final chapters address modeling issues associated with the new Basel Accord.
Inhalt
Preface.
Some Hints for Troubleshooting.
1 Estimating Credit Scores with Logit.
Linking scores, default probabilities and observed default behavior.
Estimating logit coefficients in Excel.
Computing statistics after model estimation.
Interpreting regression statistics.
Prediction and scenario analysis.
Treating outliers in input variables.
Choosing the functional relationship between the score and explanatory variables.
Concluding remarks.
Appendix.
Notes and literature.
2 The Structural Approach to Default Prediction and Valuation.
Default and valuation in a structural model.
Implementing the Merton model with a one-year horizon.
The iterative approach.
A solution using equity values and equity volatilities.
Comparing different approaches.
Implementing the Merton model with a T-year horizon.
Credit spreads.
Notes and literature.
3 Transition Matrices.
Cohort approach.
Multi-period transitions.
Hazard rate approach.
Obtaining a generator matrix from a given transition matrix.
Confidence intervals with the Binomial distribution.
Bootstrapped confidence intervals for the hazard approach.
Notes and literature.
Appendix.
4 Prediction of Default and Transition Rates.
Candidate variables for prediction.
Predicting investment-grade default rates with linear regression.
Predicting investment-grade default rates with Poisson regression.
Backtesting the prediction models.
Predicting transition matrices.
Adjusting transition matrices.
Representing transition matrices with a single parameter.
Shifting the transition matrix.
Backtesting the transition forecasts.
Scope of application.
Notes and literature.
Appendix.
5 Modeling and Estimating Default Correlations with the Asset Value Approach.
Default correlation, joint default probabilities and the asset value approach.
Calibrating the asset value approach to default experience: the method of moments.
Estimating asset correlation with maximum likelihood.
Exploring the reliability of estimators with a Monte Carlo study.
Concluding remarks.
Notes and literature.
6 Measuring Credit Portfolio Risk with the Asset Value Approach.
A default mode model implemented in the spreadsheet.
VBA implementation of a default-mode model.
Importance sampling.
Quasi Monte Carlo.
Assessing simulation error.
Exploiting portfolio structure in the VBA program.
First extension: Multi-factor model.
Second extension: t-distributed asset values.
Third extension: Random LGDs.
Fourth extension: Other risk measures.
Fifth extension: Multi-state modeling.
Notes and literature.
7 Validation of…