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Modeling the probability of default of Russian banks

Vol. 25, Iss. 6, JUNE 2019

Received: 8 November 2016

Received in revised form: 6 December 2016

Accepted: 7 December 2016

Available online: 28 June 2019

Subject Heading: Banking

JEL Classification: C51, C52, G21, G33

Pages: 1336–1352

Radionova M.V. National Research University Higher School of Economics, Perm, Russian Federation

ORCID id: not available

Pristupina Yu.V. IVS Group, Perm, Russian Federation

ORCID id: not available

Subject The article focuses on modeling of the default probability of the Russian commercial banks. The research reviews two categories of the Russian commercial banks, i.e. those ones with their licenses recalled by the Central Bank of Russia within August 2013 through May 2016 and the banks that are still in operation. We investigate the reliability and sustainability of credit institutions, and factor that fuel the default.
Objectives The research builds up an econometric model for evaluating the probability of banks' default in line with the specifics of the Russian market.
Methods Logistic regression is used to determine whether bankruptcy is possible, since it considers figures of financial statements and some institutional factors. The information framework comprises quarterly reports of the Russian commercial banks, which subsequently went bankrupt.
Results The article outlines trends in the contemporary banking system, shows key stages of setting up a model for evaluating the probability of the Russian commercial banks' default. Based on properties of the model, we made a conclusion that it was of high quality in terms of statistical significance and economic substance.
Conclusions and Relevance The findings can prove useful for researches who study bankruptcy of credit institutions, and banks' management. The model can be also practiced by banking oversight agencies of the Russian Federations for purposes of remote monitoring, and companies, which are choosing the bank for servicing their accounts. The simplicity and understandability of data allow analyzing banks from perspectives of its would-be customers.

Keywords: bank, regulation, default, bankruptcy, logistic regression


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