IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/41507.html
   My bibliography  Save this paper

What factors drive the Russian banks license withdrawal

Author

Listed:
  • Peresetsky, A. A.

Abstract

The binary and multinomial logit models are applied for prediction of the Russian banks defaults (license withdrawals) using data from bank balance sheets and macroeconomic indicators. Significantly different models correspond to the two main grounds for license withdrawal: financial insolvency and money laundering. Analysis of data for the period 2005.2–2008.4 for accurate prediction of a bank’s financial insolvency, which is the focus of interest for the Russian Deposit Insurance Agency, demonstrates that the multinomial model doesn’t outperform the binary model.

Suggested Citation

  • Peresetsky, A. A., 2011. "What factors drive the Russian banks license withdrawal," MPRA Paper 41507, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:41507
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/41507/1/MPRA_paper_41507.pdf
    File Function: original version
    Download Restriction: no

    References listed on IDEAS

    as
    1. Cole, Rebel A. & Gunther, Jeffery W., 1995. "Separating the likelihood and timing of bank failure," Journal of Banking & Finance, Elsevier, vol. 19(6), pages 1073-1089, September.
    2. Пересецкий А.А., 2007. "Методы Оценки Вероятности Дефолта Банков," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 43(3), июль.
    3. Wiginton, John C., 1980. "A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(03), pages 757-770, September.
    4. Lennox, Clive, 1999. "Identifying failing companies: a re-evaluation of the logit, probit and DA approaches," Journal of Economics and Business, Elsevier, vol. 51(4), pages 347-364, July.
    5. Izan, H. Y., 1984. "Corporate distress in Australia," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 303-320, June.
    6. Scott, James, 1981. "The probability of bankruptcy: A comparison of empirical predictions and theoretical models," Journal of Banking & Finance, Elsevier, vol. 5(3), pages 317-344, September.
    7. John Krainer & Jose A. Lopez, 2009. "Do supervisory rating standards change over time?," Economic Review, Federal Reserve Bank of San Francisco, pages 13-24.
    8. Karminsky, Alexandr & Peresetsky, Anatoly, 2007. "Models of Banks Ratings," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 5(1), pages 3-19.
    9. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    10. Koetter, M. & Bos, J.W.B. & Heid, F. & Kolari, J.W. & Kool, C.J.M. & Porath, D., 2007. "Accounting for distress in bank mergers," Journal of Banking & Finance, Elsevier, vol. 31(10), pages 3200-3217, October.
    11. Christophe J. Godlewski, 2007. "Are Ratings Consistent with Default Probabilities?: Empirical Evidence on Banks in Emerging Market Economies," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 43(4), pages 5-23, August.
    12. Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
    13. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    14. repec:bla:joares:v:18:y:1980:i:1:p:109-131 is not listed on IDEAS
    15. Rebel Cole & Jeffery Gunther, 1998. "Predicting Bank Failures: A Comparison of On- and Off-Site Monitoring Systems," Journal of Financial Services Research, Springer;Western Finance Association, vol. 13(2), pages 103-117, April.
    16. Kolari, James & Glennon, Dennis & Shin, Hwan & Caputo, Michele, 2002. "Predicting large US commercial bank failures," Journal of Economics and Business, Elsevier, vol. 54(4), pages 361-387.
    17. Rebel A. Cole & Jeffery W. Gunther, 1995. "FIMS: a new monitoring system for banking institutions," Federal Reserve Bulletin, Board of Governors of the Federal Reserve System (U.S.), issue Jan, pages 1-15.
    18. Bussiere, Matthieu & Fratzscher, Marcel, 2006. "Towards a new early warning system of financial crises," Journal of International Money and Finance, Elsevier, vol. 25(6), pages 953-973, October.
    19. Krainer, John & Lopez, Jose A, 2004. "Incorporating Equity Market Information into Supervisory Monitoring Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(6), pages 1043-1067, December.
    20. Espahbodi, Hassan & Espahbodi, Pouran, 2003. "Binary choice models and corporate takeover," Journal of Banking & Finance, Elsevier, vol. 27(4), pages 549-574, April.
    21. Anatoly Peresetsky & Alexandr Karminsky & Sergei Golovan, 2011. "Probability of default models of Russian banks," Economic Change and Restructuring, Springer, vol. 44(4), pages 297-334, November.
    22. Wei, Yingqi & Liu, Bo & Liu, Xiaming, 2005. "Entry modes of foreign direct investment in China: a multinomial logit approach," Journal of Business Research, Elsevier, vol. 58(11), pages 1495-1505, November.
    23. John Krainer & Jose A. Lopez, 2008. "Using Securities Market Information for Bank Supervisory Monitoring," International Journal of Central Banking, International Journal of Central Banking, vol. 4(1), pages 125-164, March.
    24. Beverly Hirtle & Jose A. Lopez, 1999. "Supervisory information and the frequency of bank examinations," Economic Policy Review, Federal Reserve Bank of New York, issue Apr, pages 1-20.
    25. John Krainer & Jose A. Lopez, 2002. "Off-site monitoring of bank holding companies," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue may17.
    26. Peresetsky, Anatoly, 2009. "Models for the External Support Component of Moody's Bank Ratings," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 14(2), pages 3-23.
    27. Arturo Estrella & Sangkyun Park & Stavros Peristiani, 2000. "Capital ratios as predictors of bank failure," Economic Policy Review, Federal Reserve Bank of New York, issue Jul, pages 33-52.
    28. van Soest, A.H.O. & Peresetsky, A. & Karminsky, A.M., 2003. "An Analysis of Ratings of Russian Banks," Discussion Paper 2003-85, Tilburg University, Center for Economic Research.
    29. Altman, Edward I. & Rijken, Herbert A., 2004. "How rating agencies achieve rating stability," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2679-2714, November.
    30. repec:bla:joares:v:4:y:1966:i::p:71-111 is not listed on IDEAS
    31. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    32. John Krainer & Jose A. Lopez, 2003. "How might financial market information be used for supervisory purposes?," Economic Review, Federal Reserve Bank of San Francisco, pages 29-45.
    33. Julapa Jagtiani & James Kolari & Catharine Lemieux & G. Hwan Shin, 2003. "Early warning models for bank supervision: Simpler could be better," Economic Perspectives, Federal Reserve Bank of Chicago, issue Q III, pages 49-60.
    34. Anatoly Peresetsky, Alexander Karminsky, 2011. "Models for Moody’s Bank Ratings," Frontiers in Finance and Economics, SKEMA Business School, vol. 8(1), pages 88-110, April.
    35. R. Alton Gilbert & Andrew P. Meyer & Mark D. Vaughan, 2002. "Could a CAMELS downgrade model improve off-site surveillance?," Review, Federal Reserve Bank of St. Louis, issue Jan., pages 47-63.
    36. Westgaard, Sjur & van der Wijst, Nico, 2001. "Default probabilities in a corporate bank portfolio: A logistic model approach," European Journal of Operational Research, Elsevier, vol. 135(2), pages 338-349, December.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Multinomial logit model; binary logit model; probability of default; Russian banks; money laundering; bank supervision;

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:41507. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter). General contact details of provider: http://edirc.repec.org/data/vfmunde.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.