«Дайджест-Финансы»
 

Modeling the Default Probability of the Russian Banks

Журнал «Дайджест-Финансы»
т. 22, вып. 2, июнь 2017

Получена: 08.11.2016

Получена в доработанном виде: 06.12.2016

Одобрена: 07.12.2016

Доступна онлайн: 16.06.2017

Рубрика: BANKING

Коды JEL: C51, C52, G21, G33

Страницы: 196-205

https://doi.org/10.24891/el.22.2.196

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

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

Importance 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 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 factors 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 probable, 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 conclude that it is of high quality in terms of statistical significance and economic substance.
Conclusions and Relevance The findings can prove useful for researchers 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 their would-be customers.

Ключевые слова: bank, regulation, default, bankruptcy, logistic regression

Список литературы:

  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. doi: 10.1111/j.1540-6261.1968.tb00843.x
  9. Beaver W.H. Financial Ratios as Predictors of Failure. Journal of Accounting Research, 1966, vol. 4, pp. 71–111. doi: 10.2307/2490171
  10. Meyer P., Pifer H. Prediction of Bank Failures. The Journal of Finance, 1970, vol. 25, iss. 4, pp. 853–868. doi: 10.1111/j.1540-6261.1970.tb00558.x
  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. Available at: http://dx.doi.org/10.1016/S1042-444X(01)00029-9
  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. Available at: http://dx.doi.org/10.1016/j.jce.2007.02.005
  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. Available at: http://dx.doi.org/10.1016/j.jbankfin.2008.07.016
  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. Available at: http://dx.doi.org/10.1016/S0378-4266(01)00264-3
  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. Available at: http://dx.doi.org/10.1016/j.eswa.2005.10.014
  18. Gennotte G., Pyle D. Capital Controls and Bank Risk. Journal of Banking & Finance, 1991, vol. 15, iss. 4-5, pp. 805–824. Available at: http://dx.doi.org/10.1016/0378-4266(91)90101-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.)

Посмотреть другие статьи номера »

 

ISSN 2311-9438 (Online)
ISSN 2073-8005 (Print)

Свежий номер журнала

т. 22, вып. 3, сентябрь 2017

Другие номера журнала