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Economic Change and Restructuring

, Volume 44, Issue 4, pp 297–334 | Cite as

Probability of default models of Russian banks

  • Anatoly A. Peresetsky
  • Alexandr A. Karminsky
  • Sergei V. Golovan
Article

Abstract

This paper presents results from an econometric analysis of Russian bank defaults during the period 1997–2003, focusing on the extent to which publicly available information from quarterly bank balance sheets is useful in predicting future defaults. Binary choice models are estimated to construct the probability of default model. In the first part of the paper we analyse bank survival over the financial crisis of 1998. We find that preliminary expert clustering or automatic clustering improves the predictive power of the models and incorporation of macrovariables into the models is useful. Heuristic criteria are suggested to help compare model performance from the perspectives of investors or banks supervision authorities. In the second part of the paper we use the probability of default models developed in the first part in rolling windows to analyse the Russian banking system trends after the crisis 1998.

Keywords

Banks Russia Probability of default model Early warning systems 

JEL Classification

C35 C52 F39 G21 

Notes

Acknowledgments

We are deeply grateful to the participants of the BOFIT seminar and participants of the econometrics seminar in the Center for Economic Research of the Tilburg University for insightful discussions. We also thank Tilburg University Professors Bertrand Melenberg and Arthur van Soest for many helpful comments. We are grateful for BOFIT researchers Iikka Korhonen and Tuomas Komulainen for reading the draft of the paper and their suggestions for improvement. Credit also to Andrei Petrov at the Mobile Information Agency for providing Russian bank data. We thank the students of the New Economic School involved in the bank research project. Special thanks to Greg Moore, his help makes the paper readable. A. Peresetsky worked on this paper as a visiting researcher at the Bank of Finland’s Institute for Economies in Transition (BOFIT) and would like to express his gratitude to BOFIT for the excellent, friendly research environment. Naturally, we are responsible for any erors or omissions.

References

  1. Aldrich JH, Nelson FD (1985) Linear probability, logit and profit models, Quantitative Applications in the Social Sciences Series no. 45. SAGE Publications, Beverly HillsGoogle Scholar
  2. Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Finance 23(4):589–609CrossRefGoogle Scholar
  3. Altman EI, Rijken HA (2004) How rating agencies achieve rating stability. J Bank Finance 28(11):2679–2714CrossRefGoogle Scholar
  4. Altman EI, Marco G, Varetto F (1994) Corporate distress diagnosis:Comparisons using linear discriminant analysis and neural networks (the Italian experience). J Bank Finance 18(3):505–529CrossRefGoogle Scholar
  5. Amato JD, Furfine CH (2003) Are credit ratings procyclical? BIS Working Papers No. 129. http://www.bis.org/publ/work.htm
  6. Basel Committee on Banking Supervision (2004) International convergence of capital measurement and capital standards. Bank for International Settlements, June 2004. http://www.bis.org/publ/bcbs107.htm
  7. Bobyshev AA (2001) Russian banks: typical strategies and financial intermediation. NES Best student papers series, BSP/01/047E. http://www.nes.ru/en/programs/econ/rescen/preprints/2001/bobyshev
  8. Borio C (2003) Towards a macroprudential framework for financial supervision and regulation? BIS Working Papers No. 128. http://www.bis.org/publ/work.htm
  9. Borodovsky M, Peresetsky A (1994) Deriving non-homogeneous DNA Markov chain models by cluster analysis algorithm minimizing multiple alignment entropy. Comput Chem 18(3):259–268CrossRefGoogle Scholar
  10. Bovenzi JF, Marino JA, McFadden FE (1983) Commercial bank failure prediction models, Federal Reserve Bank of Atlanta. Econ Rev 68:14–26Google Scholar
  11. Cole RA, Gunther JW (1995) Separating the likelihood and timing of bank failure. J Bank Finance 19(6):1073–1089CrossRefGoogle Scholar
  12. Cole RA, Gunther JW (1998) Predicting bank failures: a comparison of on- and off-site monitoring systems. J Fin Serv Res 13(2):103–117CrossRefGoogle Scholar
  13. Demirguc-Kunt A, Detragiache E (1998) The determinants of banking crises in developed and developing countries. IMF Staff Pap 45(1):81–109CrossRefGoogle Scholar
  14. Engelman B, Porath D (2003) Empirical comparison of different methods for default probability estimation. Quanteam Research Paper. http://www.quanteam.de/publications.html
  15. Espahbodi H, Espahbodi P (2003) Binary choice models and corporate takeover. J Bank Finance 27(4):549–574CrossRefGoogle Scholar
  16. Estrella A, Park S, Peristiani S (2000) Capital ratios as predictors of bank failure. FRBNY Econ Policy Rev 6(2):33–52Google Scholar
  17. Godlewski C (2004) Are bank ratings coherent with bank default probabilities in emerging market economies? SSRN. http://ssrn.com/abstract=588162
  18. Gunther JW, Moore RR (2003) Early warning models in real time. J Bank Finance 27(10):1979–2001CrossRefGoogle Scholar
  19. Jagtiani J, Kolari J, Lemieux C, Shin H (2003) Early warning models for bank supervision: simper could be better, Federal Reserve Bank of Chicago. Econ Perspect 27(3):49–60Google Scholar
  20. Kolari J, Glennon D, Shin H, Caputo M (2002) Predicting large US commercial bank failures. J Econ Bus 54(4):361–387CrossRefGoogle Scholar
  21. Komulainen T, Lukkarila J (2003) What drives financial crises in emerging markets? Emerg Mark Rev 4(3):248–272CrossRefGoogle Scholar
  22. Korobow L, Stuhr DP (1983) The relevance of peer groups in early warning analysis, Federal Reserve Bank of Atlanta. Econ Rev 68:27–34Google Scholar
  23. Lawrence CL, Smith LD, Rhoades M (1992) An analysis of default risk in mobile home credit. J Bank Finance 16(2):299–312CrossRefGoogle Scholar
  24. Lennox C (1999) Identifying failing companies: a reevaluation of the logit, probit and DA approaches. J Econ Bus 51(4):347–364CrossRefGoogle Scholar
  25. Löffler G (2004) An anatomy of rating through the cycle. J Bank Finance 28(3):695–720CrossRefGoogle Scholar
  26. Marchesini R, Perdue G, Bryan V (2004) Applying bankruptcy prediction models to distressed high-yield bond issues. J Fixed Income 13(4):50–56CrossRefGoogle Scholar
  27. Martin D (1977) Early warning of bank failure: a logit regression approach. J Bank Finance 1(3):249–276CrossRefGoogle Scholar
  28. Mathe C, Peresetsky A, Dehais P, van Montagu M, Rouze P (1999) Classification of Arabidopsis thaliana gene sequences: clustering of coding sequences into two groups according to codon usage improves gene prediction. J Mol Biol 285(5):1977–1991CrossRefGoogle Scholar
  29. Ohlson JA (1980) Financial ratios and the probabilistic prediction of bankruptcy. J Account Res 18(1):109–131CrossRefGoogle Scholar
  30. Peresetsky A, Кarminsky A, Golovan S (2004) Probability of default models of Russian banks. Bank of Finland, BOFIT Discussion Papers No 21/2004Google Scholar
  31. Sahajwala R, van den Bergh P (2000). Supervisory risk assessment and early warning systems. Basel committee on banking supervision. Working paper 4. http://www.bis.org/publ/bcbs_wp4.htm
  32. Scott AJ, Wild CJ (1986) Fitting logistic models under case-control or choice-based sampling. J R Stat Soc Ser B 48(2):170–182Google Scholar
  33. Segoviano MA, Lowe P (2002) Internal ratings, the business cycle and capital requirements: some evidence from an emerging market economy. BIS Working Papers, 117. http://www.bis.org/publ/work.htm
  34. Stone M, Rasp J (1991) Tradeoffs in the choice between logit and OLS for accounting choice studies. Account Rev 66(1):170–187Google Scholar
  35. van Soest AHO, Peresetsky AA, Karminsky AM (2003) An analysis of ratings of Russian banks. Tilburg University CentER Discussion Paper Series 2003/85Google Scholar
  36. Wescott SH (1984) Accounting numbers and socioeconomic variables as predictors of municipal general obligation bond ratings. J Account Res 22(1):412–423CrossRefGoogle Scholar
  37. Westgaards S, van der Wijst N (2001) Default probabilities in a corporate bank portfolio: a logistic model approach. Eur J Oper Res 135:338–349CrossRefGoogle Scholar
  38. Wiginton JC (1980) A note on the comparison of logit and discriminant models of consumer credit behaviour. J Fin Quant Anal 15(3):757–770CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2011

Authors and Affiliations

  • Anatoly A. Peresetsky
    • 1
  • Alexandr A. Karminsky
    • 2
  • Sergei V. Golovan
    • 3
  1. 1.Higher School of Economics, CEMI RAS and NESMoscowRussia
  2. 2.Higher School of EconomicsMoscowRussia
  3. 3.New Economic School and CEFIRMoscowRussia

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