Title: Portfolio Optimization with Estimation Risk
Speaker: Zhenzhen Huang
Abstract: The traditional Mean-Variance framework established by Markowitz (1952) has been the foundation of numerous research works for many years, benefiting from its mathematical tractability and intuitive clarity for investors. However, a significant limitation of this framework is its dependence on the mean vector and covariance matrix of asset returns, which are generally unknown and have to be estimated using historical data. The resulting plug-in portfolio, which relies on these estimates instead of the true parameter values, often exhibits poor out-of-sample performance due to estimation risk. While considerable research has proposed various sophisticated estimators for these two unknown parameters or introduced portfolio constraints and regularizations, this talk focuses on an alternative approach. We will discuss how to mitigate estimation risk by utilizing combined portfolios and directly optimizing the expected out-of-sample performance. Several distinct perspectives in portfolio selection will be introduced, each aimed at assessing the efficiency of combined portfolios in managing estimation risk.
