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A random effects ordered probit model for rating migrations

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  • Alsakka, Rasha
  • ap Gwilym, Owain

Abstract

Employing a random effects ordered probit model, this paper examines the sources of heterogeneity in sovereign credit ratings in emerging economies. The analysis uses data from six rating agencies for 90 countries. The model highlights the importance of considering the cross-section error, which captures country-specific heterogeneity, in modelling rating upgrades. Watchlist status is a powerful tool in predicting future rating upgrades/downgrades, and dominates rating momentum in some cases. Rating duration and existing rating are important determinants of rating migrations. Evidence of inter-agency differences and dissimilar behaviour of upgrades and downgrades is presented.

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Bibliographic Info

Article provided by Elsevier in its journal Finance Research Letters.

Volume (Year): 7 (2010)
Issue (Month): 3 (September)
Pages: 140-147

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Handle: RePEc:eee:finlet:v:7:y:2010:i:3:p:140-147

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Keywords: Random effects ordered probit model Emerging sovereign ratings Rating momentum Rating Watchlist Rating duration;

References

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  1. Al-Sakka, Rasha & ap Gwilym, Owain, 2009. "Heterogeneity of sovereign rating migrations in emerging countries," Emerging Markets Review, Elsevier, vol. 10(2), pages 151-165, June.
  2. Antonio Afonso & Pedro Gomes & Philipp Rother, 2009. "Ordered response models for sovereign debt ratings," Applied Economics Letters, Taylor & Francis Journals, vol. 16(8), pages 769-773.
  3. Fuertes, Ana-Maria & Kalotychou, Elena, 2007. "On sovereign credit migration: A study of alternative estimators and rating dynamics," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3448-3469, April.
  4. Diana Bonfim, 2006. "Credit Risk Drivers: Evaluating the Contribution of Firm Level Information and of Macroeconomic Dynamics," Economic Bulletin and Financial Stability Report Articles, Banco de Portugal, Economics and Research Department.
  5. Lando, David & Skodeberg, Torben M., 2002. "Analyzing rating transitions and rating drift with continuous observations," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 423-444, March.
  6. Kadam, Ashay & Lenk, Peter, 2008. "Bayesian inference for issuer heterogeneity in credit ratings migration," Journal of Banking & Finance, Elsevier, vol. 32(10), pages 2267-2274, October.
  7. Nickell, Pamela & Perraudin, William & Varotto, Simone, 2000. "Stability of rating transitions," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 203-227, January.
  8. Parnes, Dror, 2007. "Time series patterns in credit ratings," Finance Research Letters, Elsevier, vol. 4(4), pages 217-226, December.
  9. Bangia, Anil & Diebold, Francis X. & Kronimus, Andre & Schagen, Christian & Schuermann, Til, 2002. "Ratings migration and the business cycle, with application to credit portfolio stress testing," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 445-474, March.
  10. Bissoondoyal-Bheenick, Emawtee, 2005. "An analysis of the determinants of sovereign ratings," Global Finance Journal, Elsevier, vol. 15(3), pages 251-280, February.
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Cited by:
  1. Liang, H. & Renneboog, L.D.R., 2013. "The Foundations of Corporate Social Responsibility," Discussion Paper 2013-071, Tilburg University, Center for Economic Research.
  2. Purificacion Parrado-Martinez & Antonio Parta Ureña & Pilar Gomez Fernandez-Aguado, 2014. "Usefulness of Financial Soundness Indicators for risk assessment: The case of EU member countries," Working Papers 14.01, Universidad Pablo de Olavide, Department of Financial Economics and Accounting (former Department of Business Administration).
  3. Rasha Alsakka & Owain ap Gwilym, 2010. "Sovereign Ratings and Migrations: Emerging Markets," Working Papers 10009, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
  4. Grundel, S. & Borm, P.E.M. & Hamers, H.J.M., 2013. "Resource Allocation Problems with Concave Reward Functions," Discussion Paper 2013-070, Tilburg University, Center for Economic Research.

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