Синергия предсказательной силы моделей кредитных банковских рисковPrediction synergy of bank credit risk models

The thesis is aimed at comparing the divergence of existing credit risk models and creating a synergic model with superior forecasting power based on a rating model and probability of default model of Russian banks. The paper demonstrates that rating models, if applied alone, tend to overestimate credit risk of a bank, whereas probability of default models give underestimated results. As a result of the assignment of optimal weights and monotonic transformations to these models, the new synergic model of banks’ credit risks with higher forecasting power was obtained. Moreover, the output of the synergic model was calibrated into a probability of default using the dynamic historic default frequencies to obtain a quantitative measure of credit risk. The construction of a dynamic rating scale also allowed us to develop a credit risk minimization strategy. Investment in banks with better credit ratings is less risky right after the rating issue and is efficient to be held for the short run period. However, to minimize credit risk of capital investment in banks with highly speculative rating grades, it is optimal to choose a long run investment 1-2 years after the rating assignment.