Diversifying Estimation Errors: An Efficient Averaging Rule for Portfolio Optimization
71 Pages Posted: 11 Feb 2021 Last revised: 17 May 2021
Date Written: February 8, 2021
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 offers 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.
Keywords: Averaging; diversification; estimation error; portfolio optimization; shrinkage
JEL Classification: G11
Suggested Citation: Suggested Citation