Skip to main content
SpringerLink
Log in

Management of banking network stability taking into account industry-specific risks

  • Large Scale Systems Control
  • Published:
Automation and Remote Control Aims and scope Submit manuscript

Abstract

This paper considers an approach to loss evaluation in banks under various market crash scenarios, which is based on a classification of banks depending on the dominant industrial sector credited. The author formulates and solves the problem of optimal loan size for each industrial sector, as well as the probability minimization problem of a loan default in the case of a crisis in an industrial sector under the capital adequacy ratio norm constraint and a sufficient level of current assets. Next, the author suggests a model for evaluating possible losses due to stress situations in the banking sector and an algorithm for banks ranking based on the Snow vector method, with analysis of several typical examples. All approaches described in the paper appear novel for the Russian banking sector, as the problem of systemic risk evaluation has emerged lately in the context of recent changes in international regulations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Bulletin of Banking Statistics, Central Bank of the Russian Federation, 2008–2013, pp. 35–68.

  2. Galeev, E.M. and Tikhomirov, V.M., Optimizatsiya (Optimization), Moscow: Editorial URSS, 2000.

    Google Scholar 

  3. On the Statutory Requirements of Banks, Central Bank of the Russian Federation Instruction no. 139-i of December 3, 2012, Byull. Tsentr. Bank RF, 2012, no. 74, pp. 1–70.

    Google Scholar 

  4. Kendall, M.G. and Stuart, A., Inference and Relationship, London: Charles Griffin, 1967, 2nd ed. Translated under the title Statisticheskie vyvody i svyazi, Moscow: Nauka, 1973.

    Google Scholar 

  5. Cramér, H., Mathematical Methods of Statistics, Princeton: Princeton Univ. Press, 1946. Translated under the title Matematicheskie metody statistiki, Moscow: Mir, 1975.

    MATH  Google Scholar 

  6. Lobanov, A.A. and Chugunova, A.V., Entsiklopediya finansovogo risk-menedzhmenta (Encyclopedia of Financial Risk-Management), Moscow: Alpina Publ., 2003.

    Google Scholar 

  7. Magnus, Ya.R., Katyshev, P.K., and Peresetskii, A.A., Ekonometrika. Nachal’nyi kurs (Econometrics: A First Course), Moscow: Delo, 2004.

    Google Scholar 

  8. Milyutin, A.A., Dmitruk, A.V., and Osmolovskii, N.P., Printsip maksimuma v optimal’nom upravlenii (Maximum Principle in Optimal Control), Moscow: Mekh.-Mat. Fakul’tet Mosk. Gos Univ., 2004.

    Google Scholar 

  9. Polovinkin, E.S. and Balashov, M.V., Elementy vypuklogo i sil’no vypuklogo analiza (Elements of Convex and Strongly Convex Analysis), Moscow: Fizmatlit, 2004.

    Google Scholar 

  10. Stezhkin, A.A., Application of Optimal Control Theory to Analysis of Multiple Loan Default Situations in Transactions via a Central Contractor, Finans. Analit.: Probl. Reshen., 2013, vol. 22(160), pp. 52–60.

    Google Scholar 

  11. Altman, E., Corporate Financial Distress and Bankruptcy, New York: Wiley, 2005.

    Book  Google Scholar 

  12. Black, F. and Scholes, M., The Pricing of Options and Corporate Liabilities, J. Polit. Econ., 1973, vol. 81, no. 3, pp. 637–654.

    Article  MATH  Google Scholar 

  13. Bluhm, M. and Krahnen, J., Default Risk in an Interconnected Banking System with Endogeneous Asset Markets, Frankfurt: Goethe-Universität, 2010.

    Google Scholar 

  14. Cao, Z., Multi-CoVaR and Shapley Value: A Systemic Risk Measure, Manchester: Manchester Univ., 2013.

    Google Scholar 

  15. Greene, W., Econometric Analysis, New York: New York Univ., 2003.

    Google Scholar 

  16. Hughes, J., Efficiency in Banking: Theory, Practice, and Evidence, Pennsylvania: The Wharton School, 2008.

    Google Scholar 

  17. Jones, E.P., Mason, S.P., and Rosenfeld, E., Contingent Claims Analysis of Corporate Capital Structure: An Empirical Investigation, J. Finance, 1984, no. 39, pp. 611–625.

    Google Scholar 

  18. Kurosaki, T. and Kim, Y.S., Mean-CoAVaR Optimization for Global Banking Portfolios, Invest. Manag. Finan. Innovat., 2013, vol. 10, no. 2, pp. 15–20.

    Google Scholar 

  19. Woolbridge, J., Econometric Analysis of Cross Section and Panel Data, Cambridge: MIT Press, 2002.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Moscow Institute of Physics and Technology, Moscow, Russia

    A. A. Stezhkin

Authors
  1. A. A. Stezhkin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. A. Stezhkin.

Additional information

Original Russian Text © A.A. Stezhkin, 2013, published in Upravlenie Bol’shimi Sistemami, 2013, No. 45, pp. 264–288.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stezhkin, A.A. Management of banking network stability taking into account industry-specific risks. Autom Remote Control 76, 353–367 (2015). https://doi.org/10.1134/S0005117915020149

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0005117915020149

Keywords

Search

Navigation