Хромова Элла Павловна Синергия предсказательной силы моделей кредитных банковских рисков

Короткий опис

Хромова Элла Павловна Синергия предсказательной силы моделей кредитных банковских рисков
ОГЛАВЛЕНИЕ ДИССЕРТАЦИИ
кандидат наук Хромова Элла Павловна
Contents

Introduction

1. Review and comparative analysis of academic literature on credit risks

1.1 Underestimation of credit risk by PD models and overcautious rating assignments of CR models

1.2 Non-linear interdependence between CR and PD

1.3 Forecast combination models as a tool to improve predictive power

1.4 Comparison of different methodologies of forecasting credit risk

1.4.1 Binary and Multinomial ordered logit/probit models in detail

1.5 Factors of potential influence on credit risk of a bank

1.6 Research hypotheses

2. Difference in ratings assigned to the same issuer by various rating agencies

2.1 Formation of representative samples from the empirical data

2.1.1 Construction of the first empirical data set of international banks pre-2008 crisis period

2.1.2 Construction of the second empirical data set of international banks post-2008 crisis period

2.1.3 Construction of the third empirical data set of national banks post-2008 crisis period

2.2 Adjustment of ratings to the base scale

2.3 Construction and comparison CR models of various RAs

2.3.1 Construction of international RAs model based on the first empirical data set of international banks pret-2008 crisis period

2.3.2 Construction of international RAs model based on the second empirical data set of international

banks post-2008 crisis period

2.3.3 Construction of international and national RAs model based on the third empirical data set of national banks post-2008 crisis period

2.4 Comparison of deviations of RAs rating grades of the same issuer

3. Construction of synergic model of credit ratings and probability of defaults

3.1 Inclusion information about defaults in the sample of Russian banks

3.2 Construction of empirical models of PD and CR separately

3.2.1 Binomial and Ordered logit regressions of CR and PD models

3.2.2 Principal component analysis

3.2.3 Comparison of in-sample fit of the models

3.3 Calibration of rating scale and probability of default

3.4 Comparison of distributions of forecast errors of PD and CR models

3.5 Construction of the synergic model

3.5.1 Robustness check of the synergic model

3.6 Matching results to the existing literature

4. Dynamic calibration of CR and PD of Russian banks

4.1 Creating a dynamic scale of CR conversion to PD

4.2 Quality comparison of PD forecasts of the PD, CR and Synergic models

Conclusion

References

Appendixes