Diversifying estimation errors: An efficient averaging rule for portfolio optimization

Item Type Monograph (Working Paper)
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.

Authors Füss, Roland; Koeppel, Christian & Miebs, Felix
Language English
Keywords Averaging; diversification; estimation error; portfolio optimization; shrinkage
Subjects economics
finance
HSG Classification contribution to scientific community
HSG Profile Area SOF - System-wide Risk in the Financial System
Date 8 February 2021
Publisher SoF-HSG
Place of Publication St.Gallen
Series Name School of Finance Working Paper Series
Volume 2021/05
Number 05
Page Range 1-69
Number of Pages 69
Contact Email Address Roland.Fuess@unisg.ch
Depositing User Carolin Hitz
Date Deposited 08 Feb 2021 14:32
Last Modified 08 Feb 2021 14:32
URI: https://www.alexandria.unisg.ch/publications/262294

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Citation

Füss, Roland; Koeppel, Christian & Miebs, Felix: Diversifying estimation errors: An efficient averaging rule for portfolio optimization. School of Finance Working Paper Series, 2021, 05.

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https://www.alexandria.unisg.ch/id/eprint/262294
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