A Robust Approach to Optimal Portfolio Choice with Parameter Uncertainty

It is well known that estimated mean-variance portfolios deliver, on average, poor out-of-sample performance. A lesser-known fact that we characterize in this paper is that their out-of-sample performance is also very volatile. Using our analytical characterization of out-of-sample performance volatility, we propose a measure of portfolio robustness defined as the ratio between out-of-sample utility mean to out-of-sample utility standard deviation. We exploit our measure of portfolio robustness to calibrate shrinkage portfolios and show that they outperform those that ignore parameter uncertainty or only optimize out-of-sample utility mean