A Shrinkage Approach to Model Uncertainty and Asset Allocation
用收缩方法研究投资者厌恶模型不确定性的实证含义，发现对资本资产定价模型的不确定性厌恶会导致投资者持有非均值方差有效的组合，而强烈相信Fama-French模型时近似最优。
This article takes a shrinkage approach to examine the empirical implications of aversion to model uncertainty. The shrinkage approach explicitly shows how predictive distributions incorporate data and prior beliefs. It enables us to solve the optimal portfolios for uncertainty-averse investors. Aversion to uncertainty about the capital asset pricing model leads investors to hold a portfolio that is not mean-variance efficient for any predictive distribution. However, mean-variance efficient portfolios corresponding to extremely strong beliefs in the Fama--French model are approximately optimal for uncertainty-averse investors. The empirical Bayes approach does not result in optimal portfolios for investors who are averse to model uncertainty. Copyright 2005, Oxford University Press.