Optimal estimation for economic gains: portfolio choice with parameter uncertainty
In this paper, we advocate incorporating the economic objective function into parameter estimation by analyzing the optimal portfolio choice problem of a mean-variance investor facing parameter uncertainty. We show that, in estimating the optimal portfolio weights, the standard plug-in approach that replaces the population parameters by their sample estimates can lead to significant utility losses. While Bayesian approach accounts for parameter uncertainty by using predictive densities, its portfolio rule under diused prior makes little improvements. In contrast, our proposed new rule provides a significant improvement of utility over the plug-in approaches. We further show that with parameter uncertainty, holding the sample tangency portfolio and the riskfree asset is never optimal. An investor can benefit by holding some other risky portfolios that help reduce the estimation risk, suggesting that the presence of estimation risk completely alters the theoretical recommendation of a two-fund portfolio.
RAYMOND KAN GUOFU ZHOU
University of Toronto Washington University in St. Louis.
国际会议
昆明
英文
2005-07-05（万方平台首次上网日期，不代表论文的发表时间）