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Dynamic Mapping of Probability of Default and Credit Ratings of Russian Banks
The paper is devoted to mapping of Russian banks’ credit ratings to default probabilities for different time horizons by constructing empirical dynamic calibration scale. The paper is based on a random sample of 395 Russian banks (86 of which defaulted) for the period of 2007-2017. The scale proposed by this paper has three superior features compared to the existing scales: dynamic nature (quarterly probability of default estimates), compatibility with all rating agencies (base scale credit ratings), focus on Russian banks. As a result, the rising capital strategy was formulated. The better is a bank’s credit rating, the shorter investment horizon should be and the closer the date of investment should be to the rating assignment date in order to minimize credit risk.