Modeling the risk of bank default

Abstract

This paper is devoted to modeling the probability of default of Russian banks in 2015–2020. There are relatively few studies on defaults of Russian banks after 2015, and our work intends to partly fill this gap. The purpose of this research is to determine the main variables which significantly impact the risk of default of Russian banks. The work seeks to identify additional factors associated with an increased risk of bank defaults during a relatively stable period of development of the Russian economy (2015–2020) without external shocks, such as COVID‑19 or international sanctions. We apply an integrated approach to modeling the risk of bank defaults. Empirical methodology is represented by logit and probit models, as well as Cox regression. The set of potential predictors for bank defaults include the variables, characterizing various aspects of credit institutions functioning (in accordance with the CAMELS system), as well as macroeconomic variables. The most significant predictors of default turn out to be the capital adequacy ratio N1, bank net assets, the ratio of total loans to assets and the size of secured loan portfolio. In general, the results we obtain are consistent with the CAMELS system of indicators assessing the sustainability of commercial banks, while the impact of macroeconomic indicators tends to be insignificant. The results of the study could be of interest to the regulator both for the purposes of ongoing monitoring of financial stability as well as for default risk prevention; to credit institutions which elaborate internal systems for monitoring their financial soundness; and to financial market participants to select the most stable companies in terms of investment and allocation of funds. Further directions of research are related to the inclusion of a crisis period into the analysis and comparing the set of significant predictors for bank defaults during a crisis and a stable period of economic development, as well as the use of alternative methods, in particular, machine learning algorithms.