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Analyzing and Forecasting Credit Ratings: Some Canadian Evidence

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  • Patrick Sabourin
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    Abstract

    Can econometric tools be used successfully to predict recent downgrades or upgrades? The paper first identifies -- in constructing a credit rating model -- a relatively small number of explanatory variables from an extensive list of highly subjective criteria. Having selected a potential list of variables, an estimation procedure is then outlined that respects the ordinal but not necessarily linear nature of credit ratings. Next, the success of ordered-response models is demonstrated by replicating 75 per cent of the credit ratings assigned to the provinces by Standard and Poor's from 1976 to 1995. In a major innovation, out-of-sample forecasts are conducted over the 1996-99 period. Results indicate that the models display considerable promise by successfully predicting rating changes in various provinces. Estimates indicate that a 25-percentage point deterioration in a province's net debt-to-GDP ratio -- starting from an initial net asset position -- will result in a rating downgrade of 3 or 4 notches depending on the province's credit quality. Moving across the rating categories, there does not appear to be a debt level that triggers sudden rating changes. Finally, when the provinces are grouped according to their credit quality, an increased sensitivity to rating downgrades is identified at relatively low levels of indebtedness. Les outils économétriques permettent-ils de prévoir les changements récents, à la hausse ou à la baisse, des notations de crédit? Existe-t-il un seuil d’endettement qui entraîne des changements soudains de notation de crédit? L’auteur commence par identifier — afin de construire un modèle de notation de crédit — un nombre relativement restreint de variables économiques et financières à partir d’une liste exhaustive de critères subjectifs. Après avoir choisi une liste de variables potentielles, une méthode d’estimation est utilisée, respectant le caractère ordinal, mais pas nécessairement linéaire, des cotes de crédit. L’efficacité de la méthode des logits ordonnés est ensuite démontrée en reproduisant correctement 75 p. 100 des notations octroyées par Standard and Poor's aux provinces canadiennes entre 1976 et 1996. Comme innovation majeure, des prévisions sont ensuite effectuées à l'extérieur de la période échantillonnale pour les années 1996 à 1999. Les résultats se sont avérés extrêmement prometteurs, puisqu’il fut possible de prévoir de façon exacte plusieurs changements récents de notation de crédit dans diverses provinces. Partant d'une position initiale d'actif net, les estimations indiquent qu’une détérioration de 25 points de pourcentage du ratio dette nette au PIB d’une province entraîne une décote de trois ou quatre crans tout dépendant de la qualité de crédit de la province. Lorsqu’on passe d’une catégorie à une autre, il ne semble pas y avoir de niveau d’endettement qui déclenche des modifications soudaines de la cote d’un emprunteur public. Finalement, lorsqu’on regroupe les provinces d’après la qualité de leurs dossiers de crédit, on détecte une sensibilité accrue aux révisions de la cote de crédit à des niveaux d’endettement relativement faibles.

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    Bibliographic Info

    Paper provided by Department of Finance Canada in its series Working Papers-Department of Finance Canada with number 1999-02.

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    Handle: RePEc:fca:wpfnca:1999-02

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    Cited by:
    1. Andres Vesilind & Ingrid Toming & Raoul Lättemäe, 2001. "Determinants of Estonian Sovereign Credit Rating," Bank of Estonia Working Papers 2001-03, Bank of Estonia, revised 12 Oct 2001.

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