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Russian Bank Credit Ratings and Bank License Withdrawal 2012-2016

Author

Listed:
  • Zhivaikina, A.

    (National Research University - Higher School of Economics, Moscow, Russia)

  • Peresetsky, A.

    (National Research University - Higher School of Economics, Moscow, Russia
    Central Economics and Mathematics Institute, Russian Academy of Sciences, Moscow, Russia)

Abstract

We consider 11 credit ratings of Russian banks, assigned by international and Russian rating agencies during 2012-2016. Econometric models of these ratings designed on the public information reveal difference in the approaches of the rating agencies to the Russian bank ratings. We also design econometric models of the Russian bank defaults, where we consider default as the bank license withdrawal by the Bank of Russia. Using these models we analyze to what extent rating agencies take into account probability of the license withdrawal in short-run period and if Central Bank of the Russian Federation decisions are related to the bank ratings. We found that the international and domestic rating agencies have different attitudes to the various reasons of the bank license withdrawal formulated in the Bank of Russia orders. Models of the ratings of agencies S&P, Moody's, and Russian rating "Expert RA" show better performance than other rating models in the prediction of bank licenses withdrawals. Thus these ratings are more close to the purposes of the Bank of Russia. However binary choice models constructed by the historical data of bank licenses withdrawals beat rating models in the prediction of bank licenses withdrawals.

Suggested Citation

  • 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.
  • Handle: RePEc:nea:journl:y:2017i:36:p:49-80
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Zubarev, Andrey (Зубарев, Андрей) & Bekirova, Olga (Бекирова, Ольга), 2020. "Analysis of Bank Default Factors in 2013–2019 [Анализ Факторов Банковских Дефолтов 2013–2019 Годов]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 3, pages 106-133, June.
    2. Anna Burova & Henry Penikas & Svetlana Popova, 2021. "Probability of Default Model to Estimate Ex Ante Credit Risk," Russian Journal of Money and Finance, Bank of Russia, vol. 80(3), pages 49-72, September.
    3. Lev Fomin, 2019. "Do Higher Interest Rates on Loans and Deposits and Advertising Spending Cuts Forecast Bank Failures? Evidence from Russia," Russian Journal of Money and Finance, Bank of Russia, vol. 78(2), pages 94-112, June.
    4. 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.
    5. Mamonov, M., 2020. "Price interactions in the credit market and banks instability over the crisis and non-crisis periods in the Russian economy," Journal of the New Economic Association, New Economic Association, vol. 45(1), pages 65-110.
    6. Denis Shibitov & Mariam Mamedli, 2019. "The finer points of model comparison in machine learning: forecasting based on russian banks’ data," Bank of Russia Working Paper Series wps43, Bank of Russia.
    7. Bekirova, Olga & Zubarev, Andrey, 2023. "Determinants of risk, profitability and default probability of Russian banks," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 71, pages 20-38.

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    More about this item

    Keywords

    banks; credit ratings; rating agency; Central Bank of the Russian Federation; Russian economy; rating models; models of bank defaults;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • 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
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation

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