Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction (Quantitative Methods for Applied Economics and Business Research)

Cover......Page 1
Half-title......Page 3
Title......Page 5
Copyright......Page 6
Contents......Page 7
Figures......Page 9
Tables......Page 10
Contributors......Page 12
Advances in the modelling of credit risk and corporate bankruptcy: Introduction......Page 13
1.1 Introduction......Page 26
1.2 Models for prediction of default......Page 27
Linear discriminant analysis......Page 28
Discrete-choice models......Page 29
Censoring in the default data......Page 31
1.3 A model for evaluating an application......Page 32
The choice-based sampling problem......Page 35
Expenditure......Page 40
Default probability......Page 43
Predicted default probabilities......Page 47
Expected profit......Page 48
Aggregate decisions rules for approving or denying credit......Page 50
Ranking attributes which contribute to a denial of credit......Page 53
1.6 Conclusions......Page 54
References......Page 55
2.1 Introduction......Page 56
2.2 Building up to a mixed logit regime......Page 58
2.3 Empirical application of the mixed logit model......Page 70
2.4 Sample selection......Page 72
Explanatory variables......Page 73
Results......Page 76
Analysis of model elasticities......Page 84
Forecasting accuracy of the error component logit model......Page 87
2.5 Conclusion......Page 88
References......Page 90
3.1 Introduction......Page 92
The nested logit model......Page 95
Deriving the nested logit model......Page 98
The latent class MNL model......Page 101
An application to latent class MNL to corporate bankruptcy prediction......Page 106
3.3 Empirical illustration of the nested logit and latent class models......Page 109
3.4 Empirical results......Page 110
Forecasting accuracy of open- vs. closed-form models......Page 117
3.5 Conclusions......Page 120
Appendix......Page 122
References......Page 123
4.1 Introduction......Page 126
4.2 Survival distributions......Page 128
4.3 Benefits of survival analysis......Page 130
4.4 Non-parametric estimation......Page 133
Parametric regression models......Page 135
Semi-parametric models......Page 136
Financial distress......Page 139
Financial distress: The case of omitted dividend payments......Page 140
References......Page 145
5.1 Introduction......Page 149
5.2 Estimating probabilities with a neural network......Page 152
5.3 Sample collection and explanatory variables......Page 157
5.4 Empirical results for the neural network model......Page 158
5.5 Classifying with recursive partitioning......Page 159
5.6 Empirical results for the recursive partitioning model......Page 162
5.7 Conclusion......Page 164
References......Page 165
6.1 Introduction......Page 166
6.2 The standard Merton option model and related bankruptcy prediction models......Page 169
The standard option-pricing model of business default......Page 172
Exogenous default barrier models......Page 177
Mean-reverting leverage ratio......Page 178
Endogenous default barrier models......Page 179
6.4 Empirical illustration......Page 180
Appendix......Page 182
References......Page 184
7.1 Introduction......Page 187
7.2 First-generation structural-form models: the Merton approach......Page 188
7.3 Second-generation structural-form models......Page 190
7.4 Reduced-form models......Page 191
7.5 Credit value-at-risk models......Page 193
7.6 Recent contributions on the PD-RR relationship and their impact......Page 194
7.7 Correlation results' impact and downturn LGD......Page 199
Some final references......Page 201
7.8 Recovery ratings......Page 203
7.9 Recovery rates and procyclicality......Page 206
Early empirical evidence......Page 207
More recent evidence......Page 209
7.11 Concluding remarks......Page 212
References......Page 215
8.1 Introduction......Page 219
Total return swap......Page 222
Credit default swaps......Page 223
Credit linked notes......Page 224
Collateralized debt obligations......Page 226
Synthetic CDOs......Page 228
8.3 Growth in credit derivatives market......Page 229
The sub-prime meltdown and impacts on global debt and equity markets......Page 240
8.4 Credit derivative pricing models......Page 243
Obtaining default probabilities......Page 248
Sensitivity of CDS values to probability estimates......Page 250
References......Page 253
9.1 Introduction......Page 254
9.2 Measuring distress in local government......Page 257
Service delivery as the dependent variable......Page 259
9.3 Methodology......Page 262
Data collection and sample......Page 263
Explanatory variables......Page 264
Council characteristics......Page 265
Infrastructure variables......Page 266
9.4 Empirical results......Page 267
9.5 Conclusions......Page 275
Appendix......Page 276
References......Page 279
10.1 Introduction......Page 281
10.2 Problems with probability frameworks......Page 283
Credit ratings......Page 284
Representation of ignorance......Page 285
Knowledge versus no knowledge......Page 286
10.3 Introduction to Dempster-Shafer theory of belief functions......Page 288
The basic probability assignment function (basic belief mass function)......Page 289
Belief functions......Page 291
The measure of ambiguity......Page 292
Dempster's rule......Page 293
10.4 Risk assessment......Page 294
Default risk......Page 295
10.5 Decision making under belief functions......Page 299
Strat's approach......Page 300
Economic analysis of cost of credit ratings versus reputation cost......Page 301
10.6 Conclusion......Page 304
References......Page 305
Index......Page 307