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Diversifying estimation errors: An efficient averaging rule for portfolio optimization
Series
School of Finance Working Paper Series
Type
working paper
Date Issued
2021-02-08
Author(s)
Abstract
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.
Language
English
Keywords
Averaging
diversification
estimation error
portfolio optimization
shrinkage
HSG Classification
contribution to scientific community
HSG Profile Area
SOF - System-wide Risk in the Financial System
Publisher
SoF-HSG
Publisher place
St.Gallen
Volume
2021/05
Number
05
Start page
1
End page
69
Pages
69
Division(s)
Contact Email Address
Roland.Fuess@unisg.ch
Eprints ID
262294