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EU Banks Rating Assignments: Is There Heterogeneity between New and Old Member Countries?

  • Guglielmo Maria Caporale
  • Roman Matousek
  • Chris Stewart

We model EU countries’ bank ratings using financial variables and allowing for intercept and slope heterogeneity. Our aim is to assess whether “old” and “new” EU countries are rated differently and to determine whether “new” ones are assigned lower ratings, ceteris paribus, than “old” ones. We find that country-specific factors (in the form of heterogeneous intercepts) are a crucial determinant of ratings. Whilst “new” EU countries typically have lower ratings than “old” ones, after controlling for financial variables we also discover that all countries have significantly different intercepts, confirming our prior belief. This intercept heterogeneity suggests that each country’s rating is assigned uniquely, after controlling for differences in financial factors, which may reflect differences in country risk and the legal and regulatory framework that banks face (such as foreclosure laws). In addition, we find that ratings may respond differently to the liquidity and operating expenses to operating income variables across countries. Typically ratings are more responsive to the former and less sensitive to the latter for “new” EU countries compared with “old” EU countries.

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Article provided by Wiley Blackwell in its journal Review of International Economics.

Volume (Year): 19 (2011)
Issue (Month): 1 (02)
Pages: 189-206

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Handle: RePEc:bla:reviec:v:19:y:2011:i:1:p:189-206
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  1. Amato, Jeffery D. & Furfine, Craig H., 2004. "Are credit ratings procyclical?," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2641-2677, November.
  2. Meyer, Paul A & Pifer, Howard W, 1970. "Prediction of Bank Failures," Journal of Finance, American Finance Association, vol. 25(4), pages 853-68, September.
  3. Kolari, James & Glennon, Dennis & Shin, Hwan & Caputo, Michele, 2002. "Predicting large US commercial bank failures," Journal of Economics and Business, Elsevier, vol. 54(4), pages 361-387.
  4. Joan Jasiak & D. Feng & C. Gourieroux, 2006. "The Ordered Qualitative Model For Credit Rating Transitions," Working Papers 2006_2, York University, Department of Economics.
  5. Grunert, Jens & Norden, Lars & Weber, Martin, 2005. "The role of non-financial factors in internal credit ratings," Journal of Banking & Finance, Elsevier, vol. 29(2), pages 509-531, February.
  6. David F. Hendry & Carlos Santos, 2005. "Regression Models with Data-based Indicator Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(5), pages 571-595, October.
  7. Guglielmo Maria Caporale & Roman Matousek & Chris Stewart, 2009. "Rating Assignments: Lessons from International Banks," Discussion Papers of DIW Berlin 868, DIW Berlin, German Institute for Economic Research.
  8. Altman, Edward I. & Rijken, Herbert A., 2004. "How rating agencies achieve rating stability," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2679-2714, November.
  9. James Kolari & Michele Caputo & Drew Wagner, 1996. "Trait Recognition: An Alternative Approach to Early Warning Systems in Commercial Banking," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 23(9-10), pages 1415-1434, December.
  10. Manzoni, Katiuscia, 2004. "Modeling Eurobond credit ratings and forecasting downgrade probability," International Review of Financial Analysis, Elsevier, vol. 13(3), pages 277-300.
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