图书介绍：

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 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.

一到六章目录（共11章）

1 Estimating Credit Scores with Logit 1

Linking scores, default probabilities and observed default behavior 1
Estimating logit coefficients in Excel 4
Computing statistics after model estimation 8
Interpreting regression statistics 10
Prediction and scenario analysis 13
Treating outliers in input variables 15
Choosing the functional relationship between the score and explanatory variables 19
Concluding remarks 23
Notes and literature 24
Appendix 24

2 The Structural Approach to Default Prediction and Valuation 27

Default and valuation in a structural model 27
Implementing the Merton model with a one-year horizon 30
The iterative approach 30
A solution using equity values and equity volatilities 34
Implementing the Merton model with a T-year horizon 39
Credit spreads 44
Notes and literature 44

3 Transition Matrices 45

Cohort approach 46
Multi-period transitions 51
Hazard rate approach 53
Obtaining a generator matrix from a given transition matrix 58
Confidence intervals with the Binomial distribution 59
Bootstrapped confidence intervals for the hazard approach 63
Notes and literature 67
Appendix 67

4 Prediction of Default and Transition Rates 73

Candidate variables for prediction 73
Predicting investment-grade default rates with linear regression 75
Predicting investment-grade default rates with Poisson regression 78
Backtesting the prediction models 83
Predicting transition matrices 87
Adjusting transition matrices 88
Representing transition matrices with a single parameter 89
Shifting the transition matrix 91
Backtesting the transition forecasts 96
Scope of application 98
Notes and literature 98
Appendix 99

5 Modeling and Estimating Default Correlations with the Asset Value
Approach 103

Default correlation, joint default probabilities and the asset value approach 103
Calibrating the asset value approach to default experience: the method of
moments 105
Estimating asset correlation with maximum likelihood 108
Exploring the reliability of estimators with a Monte Carlo study 114
Concluding remarks 117
Notes and literature 117


6 Measuring Credit Portfolio Risk with the Asset Value Approach 119

A default mode model implemented in the spreadsheet 119
VBA implementation of a default-mode model 122
Importance sampling 126
Quasi Monte Carlo 130
Assessing simulation error 132
Exploiting portfolio structure in the VBA program 135
Extensions 137
First extension: Multi-factor model 137
Second extension: t-distributed asset values 138
Third extension: Random LGDs 139
Fourth extension: Other risk measures 143
Fifth extension: Multi-state modeling 144
Notes and literature