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Probability of default models of Russian banks

Author

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  • Anatoly Peresetsky
  • Alexandr Karminsky
  • Sergei Golovan

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. 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.Russian banking system trends after the crisis 1998 are analyzed with rolling regressions.
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Suggested Citation

  • 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.
  • Handle: RePEc:kap:ecopln:v:44:y:2011:i:4:p:297-334
    DOI: 10.1007/s10644-011-9103-2
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    More about this item

    Keywords

    Banks; Russia; Probability of default model; Early warning systems; C35; C52; F39; G21;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • F39 - International Economics - - International Finance - - - Other
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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