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

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  • Guglielmo Maria Caporale
  • Roman Matousek
  • Chris Stewart

Abstract

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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  • Guglielmo Maria Caporale & Roman Matousek & Chris Stewart, 2011. "EU Banks Rating Assignments: Is There Heterogeneity between New and Old Member Countries?," Review of International Economics, Wiley Blackwell, vol. 19(1), pages 189-206, February.
  • Handle: RePEc:bla:reviec:v:19:y:2011:i:1:p:189-206
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    1. Feng, D. & Gourieroux, C. & Jasiak, J., 2008. "The ordered qualitative model for credit rating transitions," Journal of Empirical Finance, Elsevier, vol. 15(1), pages 111-130, January.
    2. 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.
    3. 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.
    4. Caporale, Guglielmo Maria & Matousek, Roman & Stewart, Chris, 2012. "Ratings assignments: Lessons from international banks," Journal of International Money and Finance, Elsevier, vol. 31(6), pages 1593-1606.
    5. 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.
    6. Amato, Jeffery D. & Furfine, Craig H., 2004. "Are credit ratings procyclical?," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2641-2677, November.
    7. Manzoni, Katiuscia, 2004. "Modeling Eurobond credit ratings and forecasting downgrade probability," International Review of Financial Analysis, Elsevier, vol. 13(3), pages 277-300.
    8. Meyer, Paul A & Pifer, Howard W, 1970. "Prediction of Bank Failures," Journal of Finance, American Finance Association, vol. 25(4), pages 853-868, September.
    9. Carmen M. Reinhart, 2002. "An Introduction," The World Bank Economic Review, World Bank, vol. 16(2), pages 149-150, August.
    10. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    11. 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.
    12. Stefanescu, Catalina & Tunaru, Radu & Turnbull, Stuart, 2009. "The credit rating process and estimation of transition probabilities: A Bayesian approach," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 216-234, March.
    13. David F. Hendry, 2001. "Modelling UK inflation, 1875-1991," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 255-275.
    14. 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.
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    Cited by:

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    2. Themistokles Lazarides & Evaggelos Drimpetas, 2016. "Defining the factors of Fitch rankings in the European banking sector," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 6(2), pages 315-339, August.
    3. Salvador, Carlos & Pastor, Jose Manuel & Fernández de Guevara, Juan, 2014. "Impact of the subprime crisis on bank ratings: The effect of the hardening of rating policies and worsening of solvency," Journal of Financial Stability, Elsevier, vol. 11(C), pages 13-31.
    4. Alexander Karminsky & Richard Hainsworth & Vasily Solodkov, 2013. "Arm’s Length Method for Comparing Rating Scales," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 3(2), pages 114-135, December.
    5. Indermit S Gill & Naotaka Sugawara & Juan Zalduendo, 2014. "The Center Still Holds: Financial Integration in the Euro Area," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 56(3), pages 351-375, September.
    6. Salvador, Carlos & Fernández de Guevara, Juan & Pastor, José Manuel, 2018. "The adjustment of bank ratings in the financial crisis: International evidence," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 289-313.
    7. Ozturk, Huseyin & Namli, Ersin & Erdal, Halil Ibrahim, 2016. "Modelling sovereign credit ratings: The accuracy of models in a heterogeneous sample," Economic Modelling, Elsevier, vol. 54(C), pages 469-478.
    8. Volkova, Olga (Волкова, Ольга) & Lvova, Irina (Львова, Ирина), 2016. "The bank's rating, the rating agencies, Basel II of, financial indicator, the econometric model [Влияние Финансовых Показателей На Международные Рейтинги Российских Банков]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 1, pages 177-195, February.
    9. Shen, Chung-Hua & Huang, Yu-Li & Hasan, Iftekhar, 2012. "Asymmetric benchmarking in bank credit rating," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(1), pages 171-193.

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    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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