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

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  • 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, Research Department.
  • Handle: RePEc:cnb:wpaper:2004/01
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    References listed on IDEAS

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    1. Gropp, Reint & Vesala, Jukka & Vulpes, Giuseppe, 2006. "Equity and Bond Market Signals as Leading Indicators of Bank Fragility," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(2), pages 399-428, March.
    2. Kadri Männasoo & David G. Mayes, 2006. "Investigating the Early Signals of Banking Sector Vulnerabilities in Central and East European Emerging Markets," Chapters,in: Financial Development, Integration and Stability, chapter 21 Edward Elgar Publishing.
    3. Alexis Derviz & Narcisa Kadlcakova, 2001. "Methodological Problems of Quantitative Credit Risk Modeling in the Czech Economy," Archive of Monetary Policy Division Working Papers 2001/39, Czech National Bank.
    4. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    5. Rebel Cole & Jeffery Gunther, 1998. "Predicting Bank Failures: A Comparison of On- and Off-Site Monitoring Systems," Journal of Financial Services Research, Springer;Western Finance Association, vol. 13(2), pages 103-117, April.
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    Cited by:

    1. 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.
    2. Jan Babecky & Sofia Bauducco & Ales Bulir & Martin Cihak & Petr Jakubik & Lubos Komarek & Zlata Komarkova & Jiri Podpiera & Christian Schmieder & Laurent Weill, 2009. "CNB Economic Research Bulletin: Financial and Global Stability Issues," Occasional Publications - Edited Volumes, Czech National Bank, Research Department, edition 2, volume 7, number rb07/2 edited by Jan Babecky & Jan Frait.
    3. Kiril Tochkov & Nikolay Nenovsky, 2011. "Institutional Reforms, EU Accession, and Bank Efficiency in Transition Economies: Evidence from Bulgaria," Emerging Markets Finance and Trade, M.E. Sharpe, Inc., vol. 47(1), pages 113-129, January.
    4. 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.
    5. Alena Bicakova & Kamil Dybczak & Ales Krejdl & Jiri Slacalek & Michal Slavik, 2007. "CNB Economic Research Bulletin: Fiscal Policy and its Sustainability," Occasional Publications - Edited Volumes, Czech National Bank, Research Department, edition 2, volume 5, number rb05/2 edited by Ian Babetskii & Vladimir Bezdek.
    6. Anca Podpiera & Jiri Podpiera, 2005. "Deteriorating Cost Efficiency in Commercial Banks Signals an Increasing Risk of Failure," Working Papers 2005/06, Czech National Bank, Research Department.
    7. 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.
    8. Juraj Antal & Frantisek Brazdik & Jan Bruha & Martin Fukac & Adrian Pagan & Jiri Podpiera & Stanislav Polak & Yuliya Rychalovska, 2008. "CNB Economic Research Bulletin: Inflation Targeting and DSGE Models," Occasional Publications - Edited Volumes, Czech National Bank, Research Department, edition 2, volume 6, number rb06/2 edited by Juraj Antal & Jan Babecky.
    9. 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.
    10. 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.
    11. Henrik Andersen, 2008. "Failure prediction of Norwegian banks: A Logit approach," Working Paper 2008/02, Norges Bank.
    12. 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.

    More about this item

    Keywords

    Bank rating; CAMELS; ordered logit model; panel data analysis.;

    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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