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Increase of banks’ credit risks forecasting power by the usage of the set of alternative models

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  • Alexander M. Karminsky

    (National Research University Higher School of Economics, Moscow, Russia)

  • Ella Khromova

    (National Research University Higher School of Economics, Moscow, Russia)

Abstract

The paper is aimed at comparing the divergence of existing credit risk models and creating a synergic model with superior forecasting power based on a rating model and probability of default model of Russian banks. The paper demonstrates that rating models, if applied alone, tend to overestimate an instability of a bank, whereas probability of default models give underestimated results. As a result of the assigning of optimal weights and monotonic transformations to these models, the new synergic model of banks’ credit risks with higher forecasting power (predicted 44% of precise estimates) was obtained.

Suggested Citation

  • Alexander M. Karminsky & Ella Khromova, 2018. "Increase of banks’ credit risks forecasting power by the usage of the set of alternative models," Russian Journal of Economics, ARPHA Platform, vol. 4(2), pages 155-174, June.
    • Handle: RePEc:arh:jrujec:v:4:y:2018:i:2:p:155-174
    DOI: 10.3897/j.ruje.4.27737
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    References listed on IDEAS

    as
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    4. D Rösch & H Scheule, 2014. "Forecasting probabilities of default and loss rates given default in the presence of selection," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 393-407, March.
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    More about this item

    Keywords

    banks; credit ratings; probability of default; ordered logit models; ordered probit models; rating agencies;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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