A Theory of Credit Rating Criteria

Nan Guo (), Steven Kou (), Bin Wang () and Ruodu Wang ()
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Nan Guo: China Bond Rating Co. Ltd., Beijing 100045, China
Steven Kou: Department of Finance, Questrom School of Business, Boston University, Boston, Massachusetts 02215
Bin Wang: RCSDS, National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
Ruodu Wang: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada

Management Science, 2025, vol. 71, issue 4, 3583-3599

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.

Keywords: credit ratings; structured finance; Dodd–Frank; axiomatic characterization (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:71:y:2025:i:4:p:3583-3599

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