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Russian Banks Credit Risk Stress-Testing Based on the Publicly Available Data

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Digital Science (DSIC18 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 850))

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Abstract

This paper suggests an algorithm for stress testing of the credit risk of a Russian commercial bank, intended for use by investors and bank customers to assess the bank’s financial stability under stressful scenarios. Indicator of bank losses in this work is the indicator “loan loss provision”. An algorithm is proposed that describes the bank’s cash flows in stressful situations, taking into account the demand function for the loans of the analyzed bank, the bank’s availability of the necessary capital to increase the loan portfolio, and the availability of a sufficient amount of liquid funds to cover losses.

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Notes

  1. 1.

    www.cbr.ru/credit/forms.asp.

  2. 2.

    Before 01.2014 the form 0409134 “Calculation of own funds (capital)” was adopted.

  3. 3.

    We also impose other assumptions such as shareholders cannot invest their funds to raise a capital. Securities and bonds are not re-evaluated.

  4. 4.

    MIACR – Moscow Interbank Actual Credit Rate.

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Author information

Authors and Affiliations

  1. National Research University “Higher School of Economics”, Myasnitskaya st. 20, Moscow, Russian Federation

    Davit Bidzhoyan & Tatyana Bogdanova

  2. Springer Heidelberg, Tiergartenstr. 17, 69121, Heidelberg, Germany

    Davit Bidzhoyan & Tatyana Bogdanova

Authors
  1. Davit Bidzhoyan
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  2. Tatyana Bogdanova
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Corresponding authors

Correspondence to Davit Bidzhoyan or Tatyana Bogdanova .

Editor information

Editors and Affiliations

  1. Institute of Certified Specialists, Perm, Russia

    Tatiana Antipova

  2. Information Systems and Software Engineering, University of Coimbra, Coimbra, Portugal

    Alvaro Rocha

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Bidzhoyan, D., Bogdanova, T. (2019). Russian Banks Credit Risk Stress-Testing Based on the Publicly Available Data. In: Antipova, T., Rocha, A. (eds) Digital Science. DSIC18 2018. Advances in Intelligent Systems and Computing, vol 850. Springer, Cham. https://doi.org/10.1007/978-3-030-02351-5_31

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