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 the rating criterion for pricing. Two types of investors, simple investors and model-based investors, who use the rating information differently, are considered separately. Concepts of self-consistency and information gap are proposed to study different rating criterion. While the expected loss criterion used by Moody's satisfies self-consistency, the probability of default criterion used by S&P 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 the 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. The talk is based on joint work with Nan Guo, Steve Kou, and Bin Wang. 

Short Bio
Dr. Ruodu Wang is Tier 1 Canada Research Chair in Quantitative Risk Management and Professor of Actuarial Science and Quantitative Finance at the University of Waterloo. He received his PhD in Mathematics (2012) from the Georgia Institute of Technology, after completing his Bachelor (2006) and Master’s (2009) degrees at Peking University. He holds editorial positions in leading journals in actuarial science, operations research and mathematical economics, including Co-Editor of the European Actuarial Journal, and Co-Editor of ASTIN Bulletin - The Journal of the International Actuarial Association. Among other international awards and recognitions, he is the inaugural winner of the SOA Actuarial Science Early Career Award (2021) from the Society of Actuaries, and a Fellow of the Institute of Mathematical Statistics (elected 2022).

Tencent:

https://meeting.tencent.com/dm/BizlE9URPqMC

ID: 607-128-288