IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

What factors drive the Russian banks license withdrawal

  • Peresetsky, A. A.

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.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

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

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 41507.

as
in new window

Length:
Date of creation: 2011
Date of revision:
Handle: RePEc:pra:mprapa:41507
Contact details of provider: Postal: Schackstr. 4, D-80539 Munich, Germany
Phone: +49-(0)89-2180-2219
Fax: +49-(0)89-2180-3900
Web page: http://mpra.ub.uni-muenchen.de

More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. 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.
  2. Bussière, Matthieu & Fratzscher, Marcel, 2002. "Towards a new early warning system of financial crises," Working Paper Series 0145, European Central Bank.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. John Krainer & Jose A. Lopez, 2004. "Using securities market information for bank supervisory monitoring," Working Paper Series 2004-05, Federal Reserve Bank of San Francisco.
  8. 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.
  9. 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.
  10. 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.
  11. 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 1043-67, December.
  12. Peresetsky, A. A. & Karminsky, A. M., 2011. "Models for Moody’s bank ratings," MPRA Paper 34864, University Library of Munich, Germany.
  13. Koetter, Michael & Bos, Jaap W. B. & Heid, Frank & Kool, Clemens J. M. & Kolari, James W. & Porath, Daniel, 2005. "Accounting for distress in bank mergers," Discussion Paper Series 2: Banking and Financial Studies 2005,09, Deutsche Bundesbank, Research Centre.
  14. 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.
  15. 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.
  16. John Krainer & Jose A. Lopez, 2009. "Do supervisory rating standards change over time?," Economic Review, Federal Reserve Bank of San Francisco, pages 13-24.
  17. 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.
  18. Christophe J. Godlewski, 2007. "Are Ratings Consistent with Default Probabilities?: Empirical Evidence on Banks in Emerging Market Economies," Emerging Markets Finance and Trade, M.E. Sharpe, Inc., vol. 43(4), pages 5-23, August.
  19. 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.
  20. Beverly J. 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.
  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. Rebel Cole & Jeffery Gunther, 1998. "Predicting Bank Failures: A Comparison of On- and Off-Site Monitoring Systems," Journal of Financial Services Research, Springer, vol. 13(2), pages 103-117, April.
  23. 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.
  24. 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.
  25. Espahbodi, Hassan & Espahbodi, Pouran, 2003. "Binary choice models and corporate takeover," Journal of Banking & Finance, Elsevier, vol. 27(4), pages 549-574, April.
  26. 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.
  27. John Krainer & Jose A. Lopez, 2002. "Off-site monitoring of bank holding companies," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue may17.
  28. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
  29. Izan, H. Y., 1984. "Corporate distress in Australia," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 303-320, June.
  30. 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.
  31. Julapa Jagtiani & James Kolari & Catharine Lemieux & 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.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

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: (Ekkehart Schlicht)

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 references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link 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 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.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.