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Probability of default models of Russian banks

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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.

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Notes

  1. Violation of CBR standards, first of all the capital adequacy ratio (H1).

  2. Models for other clusters are found in Peresetsky et al. (2004), and are also available by email request.

  3. Coefficients of the separate logit models are presented at the Appendix 5.

  4. Again, data kindly provided by the Mobile Information Agency.

  5. Again as in Sect. 3.1, using a two-year lag between bank data and observed status provides the best results.

  6. For bank and financial crises, we take from Demirguc-Kunt and Detragiache (1998) and Komulainen and Lukkarila (2003). For the firm defaults, we use Engelman and Porath (2003). For the Russian banks, we follow Peresetsky et al. (2004).

  7. In the case where the investor has incentive to invest all his/her money in “reliable” banks, the optimal behavior is simply to invest all money S into one, the most “reliable” bank.

  8. The Russian Deposit Insurance Agency adopted this methodology in 2007 for the purpose of estimating the adequacy of the Deposit Insurance Fund for possible losses in each following year.

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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.

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Authors and Affiliations

  1. Higher School of Economics, CEMI RAS and NES, Nakhinmovskii pr. 47, Moscow, 117418, Russia

    Anatoly A. Peresetsky

  2. Higher School of Economics, Nakhinmovskii pr. 47, Moscow, 117418, Russia

    Alexandr A. Karminsky

  3. New Economic School and CEFIR, Nakhinmovskii pr. 47, Moscow, 117418, Russia

    Sergei V. Golovan

Authors
  1. Anatoly A. Peresetsky
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  2. Alexandr A. Karminsky
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  3. Sergei V. Golovan
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Corresponding author

Correspondence to Anatoly A. Peresetsky.

Appendices

Appendix 1

Table 19 Correlation of bank ratios, April 1998
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Appendix 2

figure 11

Distribution of Russian bank defaults, 1991–2002. (The August 1998 crisis is indicated with a black bar)

Appendix 3

Table 20 Mean values of ratios over clusters, 1998
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Appendix 4

Table 21 Model fitted for various clusters, 1998
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Peresetsky, A.A., Karminsky, A.A. & Golovan, S.V. Probability of default models of Russian banks. Econ Change Restruct 44, 297–334 (2011). https://doi.org/10.1007/s10644-011-9103-2

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