Title: ESG-constrained Portfolio Choice with Estimation Risk
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, 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 a 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 on 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.
Bio: Dr. Zhenzhen Huang 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 focuses on actuarial science, quantitative finance, and risk management, with interests in designing robust investment strategies, developing efficient risk evaluation methods, and exploring broader applications of machine learning.