+7 925 966 4690, 9am6pm (GMT+3), Monday – Friday
ИД «Финансы и кредит»

JOURNALS

  

FOR AUTHORS

  

SUBSCRIBE

    
Finance and Credit
 

Modeling the probability of default of Russian banks

Vol. 25, Iss. 6, JUNE 2019

Received: 8 November 2016

Received in revised form: 6 December 2016

Accepted: 7 December 2016

Available online: 28 June 2019

Subject Heading: Banking

JEL Classification: C51, C52, G21, G33

Pages: 1336–1352

https://doi.org/10.24891/fc.25.6.1336

Radionova M.V. National Research University Higher School of Economics, Perm, Russian Federation
m.radionova@rambler.ru

ORCID id: not available

Pristupina Yu.V. IVS Group, Perm, Russian Federation
juliaprist@gmail.com

ORCID id: not available

Subject The article focuses on modeling of the default probability of the Russian commercial banks. The research reviews two categories of the Russian commercial banks, i.e. those ones with their licenses recalled by the Central Bank of Russia within August 2013 through May 2016 and the banks that are still in operation. We investigate the reliability and sustainability of credit institutions, and factor that fuel the default.
Objectives The research builds up an econometric model for evaluating the probability of banks' default in line with the specifics of the Russian market.
Methods Logistic regression is used to determine whether bankruptcy is possible, since it considers figures of financial statements and some institutional factors. The information framework comprises quarterly reports of the Russian commercial banks, which subsequently went bankrupt.
Results The article outlines trends in the contemporary banking system, shows key stages of setting up a model for evaluating the probability of the Russian commercial banks' default. Based on properties of the model, we made a conclusion that it was of high quality in terms of statistical significance and economic substance.
Conclusions and Relevance The findings can prove useful for researches who study bankruptcy of credit institutions, and banks' management. The model can be also practiced by banking oversight agencies of the Russian Federations for purposes of remote monitoring, and companies, which are choosing the bank for servicing their accounts. The simplicity and understandability of data allow analyzing banks from perspectives of its would-be customers.

Keywords: bank, regulation, default, bankruptcy, logistic regression

References:

  1. Vasilyuk A.A., Karminskii A.M. [Modeling of Russian banks' credit ratings on the basis of financial reporting under the Russian Accounting Standards]. Upravlenie finansovymi riskami = Financial Risk Management, 2011, no. 3, pp. 194–205. (In Russ.)
  2. Golovan' S.A., Karminskii A.M., Kopylov A.V., Peresetskii A.A. Modeli veroyatnosti defolta rossiiskikh bankov. I. Predvaritel'noe razbienie bankov na klastery [Models of the Russian banks' default probability. Preliminary clustering of banks]. Moscow, NES Publ., 2003, 24 p.
  3. Golovan' S.V., Karminskii A.M., Kopylov A.V., Peresetskii A.A. Modeli veroyatnosti defolta rossiiskikh bankov. II. Vliyanie makroekonomicheskikh faktorov na ustoichivost' bankov [Models of the Russian banks' default probability. An impact of macroeconomic factors on banks' sustainability]. Moscow, NES Publ., 2004, 25 p.
  4. Karminskii A.M., Kostrov A.V., Murzenkov T.N. Modelirovanie veroyatnosti defolta rossiiskikh bankov s ispol'zovaniem ekonometricheskikh metodov [Modeling of the Russian banks' default probability through econometric methods]. Moscow, Higher School of Economics Publ., 2012, 64 p.
  5. Karminskii A.M., Peresetskii A.A., Petrov A.E. Reitingi v ekonomike: metodologiya i praktika [Ratings in economics: methodology and practice]. Moscow, Finansy and Statistika Publ., 2005, 240 p.
  6. Peresetskii A.A. [Methods to evaluate the probability of banks' default]. Ekonomika i matematicheskie metody = Economics and Mathematical Methods, 2007, vol. 43, no. 3, pp. 37–62. (In Russ.)
  7. Peresetskii A.A. Modeli prichin otzyva litsenzii u rossiiskikh bankov [Models of reasons for recalling the Russian banks' licenses]. Moscow, NES Publ., 2010, 26 p.
  8. Altman E. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 1968, vol. 23, iss. 4, pp. 189–209. URL: Link
  9. Beaver W.H. Financial Ratios as Predictors of Failure. Journal of Accounting Research, 1966, vol. 4, pp. 71–111. URL: Link
  10. Meyer P., Pifer H. Prediction of Bank Failures. The Journal of Finance, 1970, vol. 25, iss. 4, pp. 853–868. URL: Link
  11. Clare A., Priestley R. Calculating the Probability of Failure of the Norwegian Banking Sector. Journal of Multinational Financial Management, 2002, vol. 12, iss. 1, pp. 21–40. URL: Link00029-9
  12. Claeys S., Schoors K. Bank Supervision Russian Style: Evidence of Conflicts between Micro- and Macro-Prudential Concerns. Journal of Comparative Economics, 2007, vol. 35, no. 3, pp. 630–657. URL: Link
  13. Frade J. Credit Risk Modeling: Default Probabilities. Journal of Applied Finance & Banking, 2014, vol. 4, no. 4, pp. 107–125.
  14. Männasoo K., Mayes D. Explaining Bank Distress in Eastern European Transition Economies. Journal of Banking and Finance, 2009, vol. 33, no. 2, pp. 244–253. URL: Link
  15. Duffie D., Singleton K. Credit Risk: Pricing, Measurement, and Management. Princeton Series in Finance, 2003, pp. 48–120.
  16. Bongini P., Laeven L., Majnoni G. How Good Is the Market at Assessing Bank Fragility? Journal of Banking and Finance, 2002, vol. 26, iss. 5, pp. 1011–1028. URL: Link00264-3
  17. Lanine G., Vennet R. Failure Prediction in the Russian Bank Sector with Logit and Trait Recognition Models. Expert Systems with Applications, 2006, vol. 30, iss. 3, pp. 463–478. URL: Link
  18. Gennotte G., Pyle D. Capital Controls and Bank Risk. Journal of Banking & Finance, 1991, vol. 15, iss. 4-5, pp. 805–824. URL: Link90101-Q
  19. Zaghdoudi T. Bank Failure Prediction with Logistic Regression. International Journal of Economics and Financial Issues, 2013, vol. 3, no. 2, pp. 537–543.
  20. Tot'myanina K.M. [Review of default probability models]. Upravlenie finansovymi riskami = Financial Risk Management, 2011, no. 1, pp. 39–53. (In Russ.)

View all articles of issue

 

ISSN 2311-8709 (Online)
ISSN 2071-4688 (Print)

Journal current issue

Vol. 30, Iss. 3
March 2024

Archive