Peresetsky, A. A. (2011): What factors drive the Russian banks license withdrawal.
Preview |
PDF
MPRA_paper_41507.pdf Download (344kB) | Preview |
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.
Item Type: | MPRA Paper |
---|---|
Original Title: | What factors drive the Russian banks license withdrawal |
Language: | English |
Keywords: | Multinomial logit model, binary logit model, probability of default, Russian banks, money laundering, bank supervision |
Subjects: | C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C50 - General G - Financial Economics > G2 - Financial Institutions and Services > G28 - Government Policy and Regulation G - Financial Economics > G3 - Corporate Finance and Governance > G33 - Bankruptcy ; Liquidation G - Financial Economics > G2 - Financial Institutions and Services > G21 - Banks ; Depository Institutions ; Micro Finance Institutions ; Mortgages G - Financial Economics > G2 - Financial Institutions and Services > G20 - General |
Item ID: | 41507 |
Depositing User: | Anatoly A. Peresetsky |
Date Deposited: | 24 Sep 2012 20:01 |
Last Modified: | 01 Oct 2019 19:48 |
References: | Altman, E. I., Haldeman, R., & Narayanan, P. (1977). Zeta analysis: A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1(1), 29–54. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609. Altman, E. I., Marco, G., & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance, 18(3), 505–529. Altman, E. I., Rijken, H. A. (2004). How rating agencies achieve rating stability. Journal of Banking and Finance, 28, 2679–2714. Baslevent, C., Kirmanoglu, H., & Senatalar, B. (2009). Party preferences and economic voting in Turkey (now that the crisis is over). Party Politics, 15(3), 377–391. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, Empirical Research in Accounting: Selected Studies. 4, 71–111. Bovenzi, J. F., Marino, J.A., & McFadden F. E. (1983). Commercial bank failure prediction models. Federal Reserve Bank of Atlanta. Economic Review, 68, 14–26. Bussiere, M., Fratzscher, M. (2006). Towards a new early warning system of financial crises. Journal of International Money and Finance, 25, 953–973. Coats, P. K., Fant L. F. (1993). Recognizing financial distress patterns using a neural network tool. Financial Management, 22(3), 142–155. Cole, R. A., Cornyn, B. G., & Gunther, J. W. (1995a). FIMS: A new monitoring system for banking institutions. Federal Reserve Bulletin, 81(1), 1–15. Cole, R. A., Gunther, J. W. (1995b). Separating the likelihood and timing of bank failure. Journal of Banking and Finance, 19(6), 1073–1089. Cole, R. A., Gunther, J. W. (1998). Predicting bank failures: A comparison of on- and off-site monitoring systems. Journal of Financial Services Research, 13(2), 103–117. Collier, C., Forbush, S., Nuxoll, D. A., & O’Keefe, J. (2003). The SCOR system of off–site monitoring: its objectives, functioning, and performance. FDIC Banking Review, 15(3), 17–32. Correia, A., Santos, C. M., & Barros, C. P. (2007). Tourism in Latin America. A choice analysis. Annals of Tourism Research, 34(3), 610–629. Espahbodi, H., Espahbodi, P. (2003). Binary choice models and corporate takeover. Journal of Banking and Finance, 27(4), 549–574. Estrella, A., Park, S., & Peristiani, S. (2000). Capital ratios as predictors of bank failure. FRBNY Economic Policy Review, 6(2), 33–52. Gilbert, R. A., Meyer, A. P., & Vaughan, M. D. (2002). Could a CAMELS Downgrade Model Improve Off-Site Surveillance? Federal Reserve Bank of St. Louis Review, 2002, January, 47–63. Godlewski, C. J. (2007). Are Ratings Consistent with Default Probabilities?: Empirical Evidence on Banks in Emerging Market Economies. Emerging Markets Finance and Trade, 43(4), 5–23. Hirtle, B., Lopez, J. A. (1999). Supervisory information and the frequency of bank examination. FRBNY Economic Policy Review, 5(1),.1–19. Izan, H. Y. (1984). Corporate distress in Australia. Journal of Banking and Finance, 8(2), 303–320. Jagtiani, J., Kolari, J. W., Lemieux, C., & Shin, H. (2003). Early warning models for bank supervision: Simper could be better, Federal Reserve Bank of Chicago. Economic Perspectives, 27(3), 49–60. Karminsky, A. M., Peresetsky, A. A. (2007). International agencies’ ratings models. Applied Econometrics, 1, 3–19. (in Russian). 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 and Finance, 31, 3200–3217. Kolari, J. W., Glennon, D., Shin, H., Caputo, M. (2002). Predicting large US commercial bank failures. Journal of Economics and Business, 54(4), 361–387. Krainer, J., Lopez, J. A. (2002). Off-site monitoring of bank holding companies. FRBSF Economic Letter, 15. Krainer, J., Lopez, J. A. (2003). How might financial market information be used for supervisory purposes? FRBSF Economic Review, 2003, 29–45. Krainer, J., Lopez, J. A. (2004). Incorporating equity market information into supervisory monitoring models. Journal of Money, Credit, and Banking, 36(6), 1043–1067. Krainer, J., Lopez, J. A. (2008). Using securities market information for bank supervisory monitoring. International Journal of Central Banking. 4, 125–164. Krainer, J., Lopez, J. A. (2009). Do supervisory rating standards change over time? FRBSF Economic Review, 2009, 13–24. Lennox, C. (1999). Identifying failing companies: a reevaluation of the logit, probit and DA approaches. Journal of Economics and Business, 51(4), 347–364. Lin, T.-H. (2009). A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural network models. Neurocomputing, 72(16–18), 3507–3516. Martin, D. (1977). Early warning of bank failure: A logit regression approach. Journal of Banking and Finance, 1(3), 249–276. Ohlson, J.A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), 109–131. Oshinsky, R., Olin, V. (2006). Troubled banks: Why don't they all fail? FDIC Banking Review Series, 18(1), 23–44. Partnoy, F. (1999). The siskel and ebert of financial markets?: Two thumbs down for the credit rating agencies. Washington University Law Quarterly,77(3), 619–722. Peresetsky, A. A., Karminsky, A. M. (2008). Models for Moody's bank ratings. Bank of Finland, BOFIT Discussion Papers, No 17/2008. Peresetsky, A. A., Karminsky, A. M. (2011). Models for Moody’s bank ratings. Frontiers in Finance and Economics, 8(1), 88–110. Peresetsky, А. A. (2009). Measuring external support factor of Moody’s bank ratings. Applied Econometrics, 2, 3–23. (in Russian). Peresetsky, А. A., Karminsky, A. M., & Golovan, S. V. (2004a). Probability of default models of Russian banks. Bank of Finland, BOFIT Discussion Papers No 21/2004. Peresetsky, А. A., Karminsky, A. M., & Golovan, S. V. (2011). Probability of default models of Russian banks. Economic Change and Restructuring, 44(4), 297–334. Peresetsky, А. A., Karminsky, A. M., van Soest, A. H. O. (2004b). Modeling Russian Banks Ratings. Economics and Mathematical Methods, 40(4), 10–25. (in Russian) Peresetsky, А. А. (2007). Banks’ probability of default models. Economics and Mathematical Methods, 43(3), 37–62 (in Russian). Poon, W. P. H. (2003). Are unsolicited credit ratings biased downward? Journal of Banking and Finance, 27, 593–614. Roy, van P. (2006). Is there a difference between solicited and unsolicited bank ratings and, if so, why? National Bank of Belgium working paper 79/2006. Sahajwala, R., Bergh van den, P. (2000). Supervisory risk assessment and early warning systems. BIS Working Papers, 4. Scott, J. (1981). The probability of bankruptcy: A comparison of empirical predictions and theoretical models. Journal of Banking and Finance, 5, 317–344. Soest van, A. H. O., Peresetsky, A. A., & Karminsky, A. M. (2003). An analysis of ratings of Russian banks. Tilburg University CentER Discussion Paper Series, 85/2003. Wei, Y., Liu, B., & Liu, X. (2005). Entry modes of foreign direct investment in China: a multinomial logit approach. Journal of Business Research, 58, 1495–1505. Westgaards, S., Wijst van der, N. (2001). Default probabilities in a corporate bank portfolio: A logistic model approach. European Journal of Operational Research, 135, 338–349. Wiginton, J. C. (1980). A note on the comparison of logit and discriminant models of consumer credit behavior. Journal of Financial and Quantitative Analysis, 15(3), 757–770. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/41507 |