Gunter Löffler, Peter N. Posch

Credit Risk Modeling using Excel and VBA

• Gebundenes Buch

Produktbeschreibung

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

Produktdetails

• Wiley Finance Series
• Verlag: Wiley & Sons
• Artikelnr. des Verlages: 14566092000
• 2. Aufl.
• Seitenzahl: 368
• Erscheinungstermin: 31. Januar 2011
• Englisch
• Abmessung: 250mm x 175mm x 24mm
• Gewicht: 776g
• ISBN-13: 9780470660928
• ISBN-10: 0470660929
• Artikelnr.: 31187299

Autorenporträt

About the authors 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 Assistant Professor of Finance at the University of Ulm in Germany. Previously, Peter was co-head of credit treasury at a large bank, where he also traded credit derivatives and other fixed income products for the bank's proprietary books. His Ph.D. in finance on the dynamics of credit risk is from the University of Ulm. Peter has studied economics, philosophy and law at the University of Bonn.

Inhaltsangabe

Preface to the 2nd edition. Preface to the 1st edition. 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.
Logit and probit. Marginal effects. 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. Implementing the Merton model with a T -year horizon. Credit
spreads. CreditGrades. Appendix. Notes and literature. Assumptions.
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. Matrix functions. 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 Prediction of Loss Given
Default. Candidate variables for prediction. Instrument-related variables.
Firm-specific variables. Macroeconomic variables. Industry variables.
Creating a data set. Regression analysis of LGD. Backtesting predictions.
Notes and literature. Appendix. 6 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. 7 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. Dealing with parameter uncertainty. Extensions. 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. 8
Validation of Rating Systems. Cumulative accuracy profile and accuracy
ratios. Receiver operating characteristic (ROC). Bootstrapping confidence
intervals for the accuracy ratio. Interpreting caps and ROCs. Brier score.
Testing the calibration of rating-specific default probabilities.
Validation strategies. Testing for missing information. Notes and
literature. 9 Validation of Credit Portfolio Models. Testing distributions
with the Berkowitz test. Example implementation of the Berkowitz test
Representing the loss distribution. Simulating the critical chi-square
value. Testing modeling details: Berkowitz on subportfolios. Assessing
power. Scope and limits of the test. Notes and literature. 10 Credit
Default Swaps and Risk-Neutral Default Probabilities. Describing the term
structure of default: PDs cumulative, marginal and seen from today. From
bond prices to risk-neutral default probabilities. Concepts and formulae.
Implementation. Pricing a CDS. Refining the PD estimation. Market values
for a CDS. Example. Estimating upfront CDS and the 'Big Bang' protocol.
Pricing of a pro-rata basket. Forward CDS spreads. Example. Pricing of
swaptions. Notes and literature. Appendix. Deriving the hazard rate for a
CDS. 11 Risk Analysis and Pricing of Structured Credit: CDOs and
First-to-Default Swaps. Estimating CDO risk with Monte Carlo simulation.
The large homogeneous portfolio (LHP) approximation. Systemic risk of CDO
tranches. Default times for first-to-default swaps. CDO pricing in the LHP
framework. Simulation-based CDO pricing. Notes and literature. Appendix.
Closed-form solution for the LHP model. Cholesky decomposition. Estimating
PD structure from a CDS. 12 Basel II and Internal Ratings. Calculating
capital requirements in the Internal Ratings-Based (IRB) approach.
Assessing a given grading structure. Towards an optimal grading structure.
Notes and literature. Appendix A1 Visual Basics for Applications (VBA).
Appendix A2 Solver. Appendix A3 Maximum Likelihood Estimation and Newton's
Method. Appendix A4 Testing and Goodness of Fit. Appendix A5 User-defined
Functions. Index.