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Predicting Bank CAMELS and S&P Ratings: The Case of the Czech Republic

Author

Listed:
  • Alexis Derviz
  • Jiri Podpiera

Abstract

In this paper we investigate the determinants of the movements in the long-term Standard & Poors and CAMELS bank ratings in the Czech Republic during the period when the three biggest banks, representing approximately 60% of the Czech banking sector's total assets, were privatized (i.e., the time span 1998-2001). The same list of explanatory variables corresponding to the CAMELS rating inputs employed by the Czech National Bank's banking sector regulators was examined for both ratings in order to select significant predictors among them. We employed an ordered response logit model to analyze the monthly long-run S&P rating and a panel data framework for the analysis of the quarterly CAMELS rating. The predictors for which we found significant explanatory power are: Capital Adequacy, Credit Spread, the ratio of Total Loans to Total Assets, and the Total Asset Value at Risk. Models based on these predictors exhibited a predictive accuracy of 70%. Additionally, we found that the verified variables satisfactorily predict the S&P rating one month ahead.

Suggested Citation

  • Alexis Derviz & Jiri Podpiera, 2004. "Predicting Bank CAMELS and S&P Ratings: The Case of the Czech Republic," Working Papers 2004/01, Czech National Bank.
    • Handle: RePEc:cnb:wpaper:2004/01
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    Cited by:

    1. Nsiah K. Acheampong, 2013. "The Effects of Foreign Bank Entry on Financial Performance of Domestic-Owned Banks in Ghana," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 7(3), pages 93-104.
    2. repec:cnb:ocpubv:rb06/2 is not listed on IDEAS
    3. Evžen Kocenda & Martin Vojtek, 2011. "Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(6), pages 80-98, November.
    4. Sevgi Eda Tuzcu & Emrah Ertugay, 2020. "Is size an input in the mutual fund performance evaluation with DEA?," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 10(4), pages 635-659, December.
    5. Radu Muntean, 2009. "Early Warning Models for Banking Supervision in Romania," Advances in Economic and Financial Research - DOFIN Working Paper Series 39, Bucharest University of Economics, Center for Advanced Research in Finance and Banking - CARFIB.
    6. repec:cnb:ocpubv:rb07/2 is not listed on IDEAS
    7. Ekaterina Tzvetanova, 2019. "Adaptation of the Altman’s Corporate Insolvency Prediction Model – The Bulgarian Case," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 125-142.
    8. Sargu Alina Camelia & Roman Angela, 2013. "A CROSS-COUNTRY ANALYSIS OF THE BANKSâ€(tm) FINANCIAL SOUNDNESS: THE CASE OF THE CEE-3 COUNTRIES," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 357-367, July.
    9. Shi, Baofeng & Chi, Guotai & Li, Weiping, 2020. "Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach," Economic Modelling, Elsevier, vol. 85(C), pages 420-428.
    10. Serhat Yuksel & Hasan Dincer Author-Workplace-Associate Professor of Finance, School of Business and Management Sciences & Umit Hacioglu, 2015. "CAMELS-based Determinants for the Credit Rating of Turkish Deposit Banks," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 4(4), pages 01-17, October.
    11. repec:cnb:ocpubv:rb05/2 is not listed on IDEAS
    12. Henrik Andersen, 2008. "Failure prediction of Norwegian banks: A Logit approach," Working Paper 2008/02, Norges Bank.
    13. Emna Damak, 2018. "CAMELS Model With a Proposed ¡®S¡¯ for the Bank Credit Risk Rating," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(9), pages 1-69, September.
    14. repec:cnb:ocpubv:rb06/1 is not listed on IDEAS
    15. Shiva Ghasempour & Mohamadjavad Salami, 2016. "Ranking Iranian Private Banks Based on the CAMELS Model Using the AHP Hybrid Approach and TOPSIS," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 6(4), pages 52-62, October.
    16. Anca Podpiera & Jiri Podpiera, 2005. "Deteriorating Cost Efficiency in Commercial Banks Signals an Increasing Risk of Failure," Working Papers 2005/06, Czech National Bank.
    17. Fuad Aleskerov & V. Belousova & M. Serdyuk & V. Solodkov, 2008. "Dynamic Analysis of the Behavioural Patterns of the Largest Commercial Banks in the Russian Federation," ICER Working Papers - Applied Mathematics Series 12-2008, ICER - International Centre for Economic Research.

    More about this item

    Keywords

    Bank rating; CAMELS; ordered logit model; panel data analysis.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies
    • 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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