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Modeling reasons for Russian bank license withdrawal: Unaccounted factors

Author

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  • Peresetsky, Anatoly

    (Higher School of Economics, CEMI RAS, Moscow,)

Abstract
In the paper we analyze the reasons of Russian bank license withdrawal, formulated in orders of CB RF at the period 2005.2–2008.4. During this period, after establishing deposit insurance system in Russia, two main reasons were «money laundering» and «financial insolvency». We design binary choice logit models and multinomial logit models to model probability of license withdrawal one year ahead of the event. We use in model macro indicators to control for the varying economic environment and bank-specific financial indicators taken one year before the observation of the bank status. The models reveal factors important for the prediction of the license withdrawal, which are found to be different for the two reasons. Also we investigate if multinomial model outperform binary model in the bank license withdrawal forecast. We consider dynamics of impact of unaccounted factors, including human factor.

Suggested Citation

  • Peresetsky, Anatoly, 2013. "Modeling reasons for Russian bank license withdrawal: Unaccounted factors," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 30(2), pages 49-64.
    • Handle: RePEc:ris:apltrx:0209
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. 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.
    2. repec:hig:wpaper:65/fe/2018 is not listed on IDEAS
    3. Емельянов А.М. & Брюхова О.О., 2015. "Исследование Причин Отзыва Лицензий У Российских Коммерческих Банков В Посткризисный Период (2010-2011)," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 51(3), pages 41-53, июль.
    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. 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.
    6. 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.
    7. Mikhail Mamonov, 2018. "Bank's Hidden Negative Capital Before and After the Senior Management Change at the Bank of Russia," Russian Journal of Money and Finance, Bank of Russia, vol. 77(1), pages 51-70, March.
    8. M. Mamonov., 2017. "Hidden "holes" in the capital of not yet failed banks in Russia: An estimate of the scope of potential losses," VOPROSY ECONOMIKI, N.P. Redaktsiya zhurnala "Voprosy Economiki", vol. 7.
    9. 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.
    10. 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; probability of bank default models; binary choice models; multinomial choice models; money laundering;
    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
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation

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