Increasing Forecast Power of the Credit Rating Model of Russian Banks by Combining it with the Probability of Default Model

This aim of the paper is to increase the forecast power of existing models, created for estimating banks’ financial stability. It is done by estimating two most popular measures of financial stability (credit ratings and default probabilities) and, afterwards, combining those two measures in order to achieve higher accuracy of estimation and higher forecast power. First section contains literature review of articles connected with Credit Rating model and Probability of the Default model. In the next section the main hypothesis is stated and data for testing it is described. Afterwards, two ordered logit models are estimated separately with the use of Step-Wise procedure and PCA analysis and their predicted values are compared with actual figures in order to measure the forecast power. Finally, the combined model is estimated and its out-of-sample forecasting power is shown to be higher than of separate models. From the results achieved, it can be argued that the main hypothesis was proven and the combined model, which increases the forecast power, was introduced.