Professor Kai-Sheng Song invites you to attend the Master's Project Defense of Sarah Saeedi

"Optimal Portfolio Choice with Parameter Uncertainty"

Abstract:

Markowitz's (1952) Nobel-prize winning paper on Modern Portfolio Theory shows that the optimal portfolio for a mean-variance investor is a combination of the tangency portfolio and a riskless asset (two-fund separation). Although the mean-variance framework is one of the most important benchmark models used in practice today, it requires knowledge of both the mean and covariance matrix of the asset returns, which in practice are unknown and must be estimated. In this talk, I will present analytical results by Kan and Zhou that show that the standard plug-in approach, which replaces the population parameters by their sample estimates, can lead to very poor out-of-sample performance. In fact, with parameter uncertainty, I will discuss how holding the sample tangency portfolio and the riskless asset is never optimal, suggesting that the presence of estimation risk completely alters the theoretical recommendation of a two-fund portfolio.