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

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  • Alexis Derviz
  • Jiří 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.

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Bibliographic Info

Article provided by M.E. Sharpe, Inc. in its journal Emerging Markets Finance and Trade.

Volume (Year): 44 (2008)
Issue (Month): 1 (January)
Pages: 117-130

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Handle: RePEc:mes:emfitr:v:44:y:2008:i:1:p:117-130

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Web page: http://mesharpe.metapress.com/link.asp?target=journal&id=111024

Related research

Keywords: bank rating; CAMELS; ordered logit; panel data;

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Cited by:
  1. 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, August.
  2. 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.
  3. Evžen Kocenda & Martin Vojtek, 2011. "Default Predictors in Retail Credit Scoring: Evidence from Czech Banking Data," Emerging Markets Finance and Trade, M.E. Sharpe, Inc., vol. 47(6), pages 80-98, November.
  4. 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.
  5. 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, August.
  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. 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, August.

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