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The probability of default in Russian banking

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  • Alexander Karminsky
  • Alexander Kostrov

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. Copyright Eurasia Business and Economics Society 2014

Suggested Citation

  • Alexander Karminsky & Alexander Kostrov, 2014. "The probability of default in Russian banking," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 4(1), pages 81-98, June.
    • Handle: RePEc:spr:eurase:v:4:y:2014:i:1:p:81-98
    DOI: 10.1007/s40822-014-0005-2
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Alexander M. Karminsky & Ella Khromova, 2016. "Modelling banks’ credit ratings of international agencies," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 6(3), pages 341-363, December.
    2. D. Bidzhoyan S. & Д. Биджоян С., 2018. "Модель Оценки Вероятности Отзыва Лицензии У Российского Банка // Model For Assessing The Probability Of Revocation Of A License From The Russian Bank," Финансы: теория и практика/Finance: Theory and Practice // Finance: Theory and Practice, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 22(2), pages 26-37.
    3. Olga Miroshnichenko & Elena Iakovleva & Natalia Voronova, 2022. "Banking Sector Profitability: Does Household Income Matter?," Sustainability, MDPI, vol. 14(6), pages 1-19, March.
    4. Mäkinen, Mikko & Solanko, Laura, 2017. "Determinants of bank closures : Do changes of CAMEL variables matter?," BOFIT Discussion Papers 16/2017, Bank of Finland, Institute for Economies in Transition.
    5. Alexander M. Karminsky & Sergei Grishunin & Natalya Dyachkova & Maxim Bisenov, 2020. "The comparison of empirical methods for modeling credit ratings of industrial companies from BRICS countries," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 10(2), pages 333-348, June.
    6. repec:zbw:bofitp:2017_016 is not listed on IDEAS
    7. N. P. Pilnik & I. G. Pospelov & S. A. Radionov, 2020. "On Limits of the Influence of the Bank of Russia Key Rate on Indicators of the Russian Banking System," Studies on Russian Economic Development, Springer, vol. 31(2), pages 229-237, March.
    8. Mikko Makinen & Laura Solanko, 2018. "Determinants of Bank Closures: Do Levels or Changes of CAMEL Variables Matter?," Russian Journal of Money and Finance, Bank of Russia, vol. 77(2), pages 3-21, June.
    9. Mäkinen, Mikko & Solanko, Laura, 2017. "Determinants of bank closures: Do changes of CAMEL variables matter?," BOFIT Discussion Papers 16/2017, Bank of Finland Institute for Emerging Economies (BOFIT).
    10. Chau H. A. Le, 2016. "Macro-financial linkages and bank behaviour: evidence from the second-round effects of the global financial crisis on East Asia," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 6(3), pages 365-387, December.
    11. Zhivaikina, A. & Peresetsky, A., 2017. "Russian Bank Credit Ratings and Bank License Withdrawal 2012-2016," Journal of the New Economic Association, New Economic Association, vol. 36(4), pages 49-80.
    12. Nikolay Pilnik & Stanislav Radionov & Artem Yazykov, 2018. "The Optimal Behavior Model of the Modern Russian Banking System," HSE Economic Journal, National Research University Higher School of Economics, vol. 22(3), pages 418-447.
    13. Maiya Anokhina & Henry Penikas & Victor Petrov, 2014. "Identifying SIFI Determinants for Global Banks and Insurance Companies: Implications for D-SIFIs in Russia," DEM Working Papers Series 085, University of Pavia, Department of Economics and Management.
    14. Mabe, Queen Magadi & Lin, Wei, 2018. "Determinants of Corporate Failure: The Case of the Johannesburg Stock Exchange," MPRA Paper 88485, University Library of Munich, Germany.
    15. Gurova Yelena Pavlovna, 2014. "Stability of the regional banking systems in the crisis and post-crisis periods," Экономика региона, CyberLeninka;Федеральное государственное бюджетное учреждение науки «Институт экономики Уральского отделения Российской академии наук», issue 4, pages 237-245.
    16. Yelena Gurova, 2014. "Stability Of The Regional Banking Systems In The Crisis And Post-Crisis Periods," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(4), pages 237-245.

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    More about this item

    Keywords

    Probability of default; Banks; Risk-management; Default classification; G21; G24; G32;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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