A Theory of Credit Rating Criteria

Abstract

We propose a theory for rating financial securities in the presence of structural maximization by the issuer in a market with investors who rely on credit rating. Two types of investors, simple investors who price tranches solely based on the ratings and model-based investors who use the rating information to calibrate models, are considered. Concepts of self-consistency and information gap are proposed to study different rating criteria. In particular, the expected loss criterion used by Moody’s satisfies self-consistency, but the probability of default criterion used by Standard & Poor’s does not. Moreover, the probability of default criterion typically has a higher information gap than the expected loss criterion. Empirical evidence in the post–Dodd–Frank period is consistent with our theoretical implications. We show that a set of axioms based on self-consistency leads to a tractable representation for all self-consistent rating criteria, which can also be extended to incorporate economic scenarios. New examples of self-consistent and scenario-based rating criteria are suggested. This paper was accepted by Agostino Capponi, finance. Funding: This work was supported by the National Key Research and Development Program of China [Grant 2020YFA0712700], the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2018-03823, RGPAS-2018-522590], and the National Natural Science Foundation of China [Grant 12371476]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01075 .