Diversifying estimation errors: An efficient averaging rule for portfolio optimization

We propose an averaging rule that combines established minimum-variance strategies to minimize the expected out-of-sample variance. Our rule overcomes the problem of selecting the “best” strategy ex-ante and diversifies remaining estimation errors of the single strategies included in the averaging. Extensive simulations show that the contributions of estimation errors to the out-of-sample variances are uncorrelated between the considered strategies. This implies that averaging over multiple strategies o˙ers sizable diversification benefits. Our rule leverages these benefits and compares favorably to eleven strategies in terms of out-of-sample variance on both simulated and empirical data sets. The Sharpe ratio is across all data sets at least 25% higher than for the 1/N portfolio.