Mean-Variance Efficient Portfolio Incorporating Return Prediction
Prof. Yingying LI
Chair Professor
Hong Kong University of Science and Technology
Date: 29 October 2024 (Tuesday)
Time: 10:30 to 12:00
Venue: E22-G008
Host: Prof. Yi DING, Assistant Professor in Business Economics
Abstract
We propose a novel method for estimating portfolios that leverages conditional information to achieve unconditional mean-variance efficiency in high-dimensional settings. Our methodology employs sparse regression techniques within an equivalent regression framework tailored to a conditional portfolio optimization problem. This approach is meticulously constructed to satisfy a predefined unconditional risk constraint while maximizing the expected return of the portfolio. Under mild assumptions, our method asymptotically attains unconditional mean-variance efficiency. We demonstrate the superior performance of our proposed method through comprehensive simulations and empirical studies. This talk is based on joint work with Ruizhao Huang and Xinghua Zheng.
Speaker
Prof. Yingying LI is Chair Professor at the Department of Information System, Business Statistics and Operations Management and the Department of Finance at Hong Kong University of Science and Technology. Prof. Li’s research focuses on statistical learning for financial big data, large portfolio optimization, individualized financial decision making, volatility estimation and prediction. Prof. Li is an RGC Senior Research Fellow, elected Fellow of the Society for Financial Econometrics (SoFiE). She is serving or has served as an associate editor for the Journal of American Statistical Association, Journal of Econometrics, Journal of Business & Economic Statistics and Journal of Financial Econometrics .
All are welcome!
