An averaging framework for minimum-variance portfolios: Optimal rules for combining portfolio weights
Series
School of Finance Working Paper Series
Type
working paper
Date Issued
2021-02-08
Author(s)
Abstract
We propose an averaging framework for combining minimum-variance strategies to either minimize the expected out-of-sample variance or maximize the expected out-of-sample Sharpe ratio. Our framework overcomes the problem of selecting the “best” strategy ex-ante by optimally averaging over portfolio weights. This averaging procedure has an intuitive economic interpretation because it resembles a fund-of-fund approach, where each minimum-variance strategy represents a single fund. In a range of simulations, for a set of well-established strategies, we show that optimally averaging over portfolio weights improves the out-of-sample variance and Sharpe ratio. We confirm the finding of our simulation study on empirical data.
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
65
Pages
65
Contact Email Address
Roland.Fuess@unisg.ch
Eprints ID
262294
File(s)![Thumbnail Image]()
open.access
Name
Averaging_Framework_MVP_Full_Manuscript.pdf
Size
753.67 KB
Format
Adobe PDF
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