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

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  • Alexis Derviz
  • JiÅí Podpiera

Abstract

This paper investigates the determinants of the movements in the capital-assets-management-earnings-liquidity-sensitivity to market risk (CAMELS) and the longterm Standard & Poors (S&P) bank ratings in the Czech Republic during the periods when the three largest banks, representing approximately 60 percent of the Czech banking sector's total assets, were first privatized (1998-2001) and then had sufficient time to operate under new owners (2002-2005). The same list of explanatory variables employed by the Czech National Bank's banking sector regulators, corresponding to the inputs of the CAMELS rating, are examined for both ratings to select their significant predictors. We employ an ordered-response logit model to analyze the long-run S&P rating and a standard panel data framework for the CAMELS rating. We find significant explanatory power for capital adequacy, funding spread, the ratio of total loans to total assets, the value-at-risk for total assets, and leverage.

Suggested Citation

  • Alexis Derviz & JiÅí Podpiera, 2008. "Predicting Bank CAMELS and S&P Ratings: The Case of the Czech Republic," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 44(1), pages 117-130, January.
  • Handle: RePEc:mes:emfitr:v:44:y:2008:i:1:p:117-130
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    Cited by:

    1. 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, September.
    2. 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.
    3. 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.
    4. 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, September.
    5. 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.
    6. 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, September.
    7. Henrik Andersen, 2008. "Failure prediction of Norwegian banks: A Logit approach," Working Paper 2008/02, Norges Bank.
    8. 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.
    9. 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.
    10. 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; panel data;

    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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