Abstract
The article deals with the problem of modeling and forecasting the revocation of the bank’s license depending on the volatility of macroeconomic variables. The urgency of this problem is due to the following reasons. First, the Central Bank of theRussian Federationtoday pursues a policy of clearing the banking sector from unscrupulous participants in the banking market and from banks with weak economic positions. Secondly, the strong fl in the values of macroeconomic variables over the previous few years affect the financial condition of the bank, which is the basis for the decision to revoke the license. The purpose of the article is to develop a model for assessing the probability of revocation of a license from the Russian bank on the basis of its public financial statements, taking into account the volatility of macroeconomic variables. The author has developed a logistic regression model for assessing the probability of revocation of a license from the Russian bank taking into account the volatility of macroeconomic variables. To level the effect of multicollinearity in the data, we use RIDGE modification of the logistic regression model with a certain algorithm for setting the penalty factor. The model is based on the data of official public bank statements, data on macroeconomic variables, and data on license revocations by the Bank of Russia as well. To aggregate the information and bring it into a single format, an information and logical model for the formation of the information base of the study is developed. The obtained model for assessing the probability of revocation of a license from the Russian bank has a high prognostic ability. The hypothesis of statistical difference of coefficients from zero is accepted when indicators of volatility of macroeconomic variables were at significance levels of 0.01 and above. The author concluded that the volatility of macroeconomic variables has a significant impact on the fi condition of the bank. The Bank of Russia takes this into account when deciding whether to revoke a license, as the fi condition is one of the key aspects. This approach can be used by the bank’s counterparties in assessing its reliability.
Highlights
AB S T R A CT The article deals with the problem of modeling and forecasting the revocation of the bank’s license depending on the volatility of macroeconomic variables
The strong fluctuations in the values of macroeconomic variables over the previous few years affect the financial condition of the bank, which is the basis for the decision to revoke the license
The purpose of the article is to develop a model for assessing the probability of revocation of a license from the Russian bank on the basis of its public financial statements, taking into account the volatility of macroeconomic variables
Summary
А Н Н ОТА Ц И Я В статье рассматривается проблема моделирования и прогнозирования отзыва лицензии банка в зависимости от показателей волатильности макроэкономических переменных. Цель статьи — разработка модели оценки вероятности отзыва лицензии у российского банка на основе его публичной финансовой отчетности с учетом волатильности макроэкономических переменных. Автором разработана логистическая регрессионная модель оценки вероятности отзыва лицензии у российского банка с учетом волатильности макроэкономических переменных. Полученная модель оценки вероятности отзыва лицензии у российского банка обладает высокой прогностической способностью. С., Модель оценки вероятности отзыва лицензии у российского банка. Вследствие всего вышесказанного, прогнозирование вероятности отзыва лицензии у банков России на основе оценки их финансового состояния особенно актуально для юридических лиц, являющихся клиентами банка. Карминского [5] разработан подход для оценки вероятности дефолта российских банков с отрицательным капиталом. Что включение макроэкономических переменных в модель оценки вероятности дефолта корпоративных заемщиков позволяет существенно улучшить модель для целей риск-менеджмента. Кострова [10] также построены модели оценки вероятности дефолта российских банков.
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