Reverse-engineering Country Risk Ratings: A combinatorial non-recursive model
by Peter L. Hammer of Rutgers University,
September 1, 2008
Abstract: The central objective of this paper is to develop a transparent, consistent, self contained, and stable country risk rating model, closely approximating the country risk ratings provided by Standard and Poor's (S&P). The model should be non-recursive, i.e., it should not rely on the previous years' S&P ratings. The set of variables selected here includes not only economic-financial but also political variables. We propose a new model based on the novel combinatorial-logical technique of Logical Analysis of Data (which derives a new rating system only from the qualitative information representing pairwise comparisons of country riskiness). We also develop a method allowing to derive a rating system that has any desired level of granularity. The accuracy of the proposed model's predictions, measured by its correlation coefficients with the S&P ratings, and confirmed by k-folding cross-validation, exceeds 95%. The stability of the constructed non-recursive model is shown in three ways: by the correlation of the predictions with those of other agencies (Moody's and The Institutional Investor), by predicting 1999 ratings using the non-recursive model derived from the 1998 dataset applied to the 1999 data, and by successfully predicting the ratings of several previously non-rated countries. This study provides new insights on the importance of variables by supporting the necessity of including in the analysis, in addition to economic variables, also political variables (in particular "political stability"), and by identifying "financial depth and efficiency" as a new critical factor in assessing country risk.
Keywords: combinatorial optimization, logical analysis of data, rating, credit risk, country risk.
Published in: Annals of Operations Research, Vol. 188, No. 1, (August 2011), pp. 185-213.
Previously titled: Country Risk Ratings: Statistical and Combinatorial Nonrecursive Models