Performance Evaluation under Adverse Selection and Correlation Ambiguity

44 Pages Posted: 27 Feb 2021

See all articles by Yu Huang

Yu Huang

Shanghai Jiao Tong University (SJTU) - Shanghai Advanced Institute of Finance (SAIF)

Ning Zhang

University of Reading - Henley Business School

Date Written: December 25, 2020

Abstract

We develop a model wherein a risk-neutral but ambiguity-averse principal contracts with a risk-averse agent who has a risky project. Both the agent and the principal can observe the project output and a public signal. The correlation between the output and the public signal is private information to the agent but is an ambiguous random variable to the principal. Then, apart from moral hazard, the optimal contract takes into consideration both adverse selection and ambiguity aversion simultaneously. Due to the classic trade-off between rent and efficiency, the principal lowers contract power for the agent with a low correlation project (the l-type agent) and compensates her for luck. However, aversion to correlation ambiguity counteracts with this rent reducing effect by making the principal weight the l-type agent more. Consequently, although ambiguity lessens the principal's value, it could improve social welfare by increasing efficiency of the l-type agent. We further extend the model by incorporating an aggregate signal whose variance depends on the ambiguous distribution of all the projects and allowing the agents to be ambiguity-averse. In this case, the principal has to respect the agent's model choice and compensates her for ambiguity premium, which again decreases contract power. With ambiguity-sharing, the pair of optimal separating contracts is metamorphosed compared to those in the baseline model.

Keywords: Optimal contract, Adverse selection, Ambiguity, Performance evaluation, Managerial compensation

JEL Classification: D80, D82, E24, G32, J33, J44

Suggested Citation

Huang, Yu and Zhang, Ning, Performance Evaluation under Adverse Selection and Correlation Ambiguity (December 25, 2020). Available at SSRN: https://ssrn.com/abstract=3755190 or http://dx.doi.org/10.2139/ssrn.3755190

Yu Huang (Contact Author)

Shanghai Jiao Tong University (SJTU) - Shanghai Advanced Institute of Finance (SAIF) ( email )

Shanghai Jiao Tong University
211 West Huaihai Road
Shanghai, 200030
China

Ning Zhang

University of Reading - Henley Business School ( email )

icma centre, University of Reading
Reading, rg6 6ba
United Kingdom
7492808128 (Phone)

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