Abstract
We compare several models for estimating the default probabilities of Russian banks using national statistics from 1998 to 2011, and find that a binary logit regression with a quasi-panel data structure works best. The results indicate that there is a quadratic U-shaped relationship between a bank’s capital adequacy ratio and its probability of default. In addition, macroeconomic, institutional, and time factors significantly improve model accuracy. These results are useful for national financial regulatory authorities, as well as for risk-managers in commercial banks.
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Notes
A detailed overview of PD model types is beyond the scope of the literature review. Please see Totmyanina (2011) for a more detailed explanation.
Interfax database: http://www.spark-interfax.ru, “BankScope” database: https://bankscope2.bvdep.com, "Banks and Finance" database has no official description in the Internet.
For additional details on this topic, please see Greene (2007).
There were no corrections for observations of the "default" class. In this case, extraordinary values could be caused by a weak financial position of a bank.
References
Anzoategui, D., Peria, M., & Melecky, M. (2012). Bank competition in Russia: An examination at different levels of aggregation. Emerging Markets Review, 13(1), 52–53.
Bluhm, C., Overbeck, L., & Wagner, C. (2010). Introduction to credit risk modeling. London: Chapman and Hall/CRC.
Bock, R., & Demyanets, A. (2012). Bank asset quality in emerging markets: Determinants and spillovers. IMF Working Papers, 12/71.
Chernykh, L., & Theodossiou, A. (2011). Determinants of bank long-term lending behavior: Evidence from Russia. Multinational Finance Journal, 15(3/4), 193–216.
Claeys, S., & Schoors, K. (2007). Bank supervision Russian style: Evidence of conflicts between micro- and macro-prudential concerns. Journal of Comparative Economics, 35(3), 630–657.
Clarke, G., Cull, R., & Shirley, M. (2005). Bank privatization in developing countries: A summary of lessons and findings. Journal of Banking & Finance, 29(8/9), 1905–1930.
Fungacova, Z., & Solanko, L. (2009). Risk-taking by Russian banks: Do location, ownership and size matter? BOFIT Discussion Papers, 21/2008.
Fungacova, Z., & Weill, L. (2009). How market power influences bank failures: Evidence from Russia. BOFIT Discussion Papers, 12/2009.
Greene, W. (2007). Econometric analysis (6th ed.). New Jersey: Prentice Hall.
Hainsworth, R., Karminsky, A., & Solodkov, V. (2013). Arm’s length method for comparing rating scales. Eurasian Economic Review, 3(2), 114–135.
Hanafi, M., Santi, F., & Muazaroh, (2013). The impact of ownership concentration, commissioners on bank risk and profitability: Evidence from Indonesia. Eurasian Economic Review, 3(2), 183–202.
He, H., & Edwardo, A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284.
Hosmer, D., & Lemeshow, S. (2000). Applied logistic regression. New York: Wiley.
Karminsky, A. (2010). Повышение устойчивости и эффективности банковской системы [Improving stability and efficiency of the Russian banking system], in V. Polterovich Стратегия модернизации российской экономики [Modernization strategy for Russian economy], Saint Petersburg, Aletheia.
Karminsky, A., Peresetsky, A., & Petrov, A. (2005). Рейтинги в экономике: методология и практика [Ratings in economics: Methodology and practice]. Moscow: Finansi i Statistika.
Lanine, G., & Vennet, R. (2006). Failure prediction in the Russian bank sector with logit and trait recognition models. Expert Systems with Applications, 30(3), 463–478.
Mannasoo, K., & Mayes, D. (2009). Explaining bank distress in Eastern European transition economies. Journal of Banking & Finance, 33(2), 244–253.
Micco, A., Panizza, U., & Yanez, M. (2007). Bank ownership and performance. Does politics matter? Journal of Banking & Finance, 31(1), 219–241.
Peresetsky, А. (2010). Модели причин отзыва лицензий российских банков [Modeling license revocations of Russian banks]/Preprint #WP/2010/085. New Economic School.
Peresetsky, A., Karminsky, A., & Golovan, S. (2011). Probability of default models of Russian banks. Economic Change and Restructuring, 44(4), 297–334.
Tabak, B., Craveiro, G., and Cajueiro, D. (2011). Bank efficiency and default in Brazil: Causality tests. The Central Bank of Brazil Working Paper Series, 253.
Totmyanina, K. (2011). Обзор моделей вероятности дефолта [Review of probability of default models]. Journal of Financial Risk Management, 1(25), 3–17.
Vernikov, A. (2011). Government banking in Russia: Magnitude and new features. IWH Discussion Papers, 13.
Yigit, I., & Behram, N. (2013). The relationship between diversification strategy and organizational performance in developed and emerging economy. Eurasian Business Review, 3(2), 121–136.
Acknowledgements
The work is partially supported by the International Laboratory of Quantitative Finance, NRU HSE, RF government Grant, ag. 14.A12.31.0007; a Research project No. 05-0030 ≪Analysis of credit institutions insolvency≫, NRU HSE, RF.
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Karminsky, A.M., Kostrov, A. The probability of default in Russian banking. Eurasian Econ Rev 4, 81–98 (2014). https://doi.org/10.1007/s40822-014-0005-2
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DOI: https://doi.org/10.1007/s40822-014-0005-2