La metodología de rating “through the cycle”: aplicación para la estimación de ratings soberanos

Pena, Alejandro and Rodríguez, Analía (2008): La metodología de rating “through the cycle”: aplicación para la estimación de ratings soberanos.

Abstract

This paper analyses the through-the-cycle rating concept; basically, we try to specify its main characteristics, focusing on the differences with point-in-time ratings. We also discuss the effects of this methodology on the prediction power of default probabilities, on the stability of those ratings, and their impact on the capital requirements that emerge from Basel II, in terms of their potential procyclicality. On the other hand, we argue how predictable rating changes are, and the ability of the agencies to look through the cycle when assigning qualifications. Based on that, we conclude about the way that economical fundamentals must be incorporated in rating calculations. We estimate a panel data model with random effects ordered probit, using data for the period 1997-2007.

• Item Type: MPRA Paper
• English Title: The Rating Agencies' Through-the-cycle Methodology: an application to sovereign ratings
• Language: English
• Keywords: Credit Rating Methodology, Panel Data Ordered Probit,
• Subjects: F - International Economics > F3 - International Finance > F30 - General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C25 - Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities; G - Financial Economics > G2 - Financial Institutions and Services > G20 - General

References

Afonso, A; Gomes, P; Rother, P (2007), “What “hides” behind sovereign debt ratings?” European Central Bank, Working Paper Series Nº 711 http://www.ecb.eu/pub/pdf/scpwps/ecbwp711.pdf

Altman, E; Kao, D.L. (1992), “Rating drift in high-yield bonds”, Journal of Fixed Incomemarch 1992, pp. 15-20

Altman, E; Rijken, H (2005), “The effects of rating through the cycle on rating stability, rating timeliness and default prediction performance” http://pages.stern.nyu.edu/~ealtman/TheEffectsofRatingThroughtheCycleonRatingStability.pdf

Arellano, M; Bover, O (1990), “La econometría de datos de panel”, Investigaciones Económicas (Segunda Época), Vol. XIV Nº1 Págs. 3-45

Bissoondoyal-Bheenick, E; Brooks, R; Yip, A (2005),” Determinants of Sovereign Ratings: A comparison of case-based reasoning and ordered probit approaches” Department of Econometrics and Business Statistics, Australia, Monash University http://ideas.repec.org/p/msh/ebswps/2005-9.html

Butler, J; Moffit, R (1982), “A computationally efficient quadrature procedure for the onefactor miltinomial probit model”. Econometrica 50: 761-764.

Cantor, R; Packer, F (1996), “Determinants and impact of sovereign credit ratings” Economic Policy Review, 2, 37-53. Federal Reserve Bank of New York.

Chamberlain, G (1980), “Analysis of Covariance with Qualitative Data” Review of Economic Studies 47, pp. 225-238

Fondo Monetario Internacional (2008), “Uruguay: 2007 Article IV Consultation - Staff Report; Staff Statement; Public Information Notice on the Executive Board Discussion; and Statement by the Executive Director for Uruguay”, IMF Country Report Nº 08/45.

Fréchette, G.R (2001), “Random-effects ordered probit”, Stata Technical Bulletin Reprints 59. sg158, págs 23-27. http://www.stata.com/products/stb/journals/stb59.pdf

Fréchette, G.R (2001), “Update to random-effects ordered probit”, Stata Technical Bulletin Reprints 61. sg158.1, págs 266-267. http://www.stata.com/products/stb/journals/stb61.pdf

Godoy, S (2006), “Determinantes de la clasificación de riesgo soberano de las economías emergentes”, Banco Central de Chile, Economía chilena, Vol. 9, Nº. 3, 2006, págs. 109- 123. http://dialnet.unirioja.es/servlet/articulo codigo=2212598

Heiss, F (2007), “Limited Dependent Panel Data Models with AR(1) error components in Stata” – preliminary - http://www.florian-heiss.de/stata/arldv.pdf

Ikbal, K ; Ahah, A (2008), “How do Worldwide Governance Indicators measure up ? http://siteresources.worldbank.org/PSGLP/Resources/Howdoworldwidegovernanceindicat orsmeasureup.pdf

Lando, D; Skodeberg, T (2002), “Analyzing rating transitions and rating drift with continuos observations”, Journal of Banking and Finance 26 (2/3), 423-444.

Löffler, G (2004), “An anatomy of rating through the cycle” Journal of Banking and Finance 28, 695-720. www.sciencedirect.com

Löffler, G (2005), “Can rating agencies look through the cycle?” http://www.greta.it/credit/credit2005/Friday/05_Loeffler.pdf

Merton,R. (1978), “On the pricing of corporate debt: ”The risk structure on interest rates” Journal of Finance,29,449-470

Mihaljek, D (2005) “EU entrants, candidates and aspirants: How large are the differences and how quickly could they be reduced? http://hrcak.srce.hr/index.php lang=en&show=clanak&id_clanak_jezik=15989

Moody’s (2003), “Sovereign Rating Methodology: the Implications of Highly Dollarized Banking Systems for Sovereign Credit Risk”, www.moodys.com

Moody’s (2004), “A Quantitative Model for Foreign Currency Government Bond Ratings”, www.moodys.com

Moody’s (2007), Statistical Handbook-Country Credit, Moody’s Investor’s Service.

Reinhart, C; Rogoff, K (2008), “The forgotten history of domestic debt” National Bureau of Economic Research, WP 13946. http://www.nber.org/papers/w13946

Sribney, W (1995), “Random-effects Probit” Stata Technical Bulletin Reprints 26, sg159, págs 15-18. http://www.stata.com/products/stb/journals/stb26.pdf

Standard&Poors, “Rating Methodology:Evaluating the Issuer” http://www2.standardandpoors.com/spf/pdf/fixedincome/methodology.pdf

Standard&Poors (2006), “Calificaciones Crediticias Soberanas: Un resumen” www.standardandpoors.com

Wooldridge, J (2002), “Econometric Analysis of Cross Section and Panel Data”, MIT Press.

Wooldridge, J (2005), “Introductory Econometrics: A Modern Approach”, 2da Edición, South Western Publishing Co.