ESG-constrained portfolio choice with estimation risk

Monday, 21 July 2025

10:30 AM – 12 PM

Venue: Gaia Lecture Theatre 3 (#ABS-02-LT3) 

Chairperson: Asst Prof Jinggong Zhang

Abstract

Environmental, Social, and Governance (ESG) investing has emerged as a global trend, offering sustainable benefits to investors and financial institutions. This study integrates an ESG constraint into the classical mean-variance optimization framework in the case without a risk-free asset, while accounting for the estimation risk associated with the first two moments of asset returns. We begin by examining the problem in the absence of estimation risk and deriving the optimal portfolio characterized by three-fund separation. To address estimation risk, we propose a combined three-fund portfolio, with components based on the plug-in ESG portfolio. The optimal combination coefficients are derived by maximizing the expected out-of-sample mean-variance utility. Furthermore, we provide a comparative performance analysis of the ESG-constrained portfolios involved in the study. Extensive simulations and empirical analysis demonstrate that the combined portfolio outperforms the plug-in ESG portfolio in terms of certainty equivalent return.

About the Speaker

Huang Zhenzhen is an assistant professor at The Ohio State University. She received her Ph.D. in Actuarial Science from the University of Waterloo in 2024. Her research interests include actuarial science, quantitative finance, risk management, and broader applications of machine learning. Specifically, her current research focuses on designing robust investment strategies under parameter uncertainty and developing efficient risk assessment methods.