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Mean-semivariance portfolio optimization using minimum average partial

Abstract
Abstract Mean-semivariance and minimum semivariance portfolios are a preferable alternative to mean-variance and minimum variance portfolios whenever the asset returns are not symmetrically distributed. However similarly to other portfolios based on downside risk measures they are particularly affected by parameter uncertainty because the estimates of the necessary inputs are less reliable than the estimates of the full covariance matrix. We address this problem by performing PCA using Minimum Average Partial on the downside correlation matrix in order to reduce the dimension of the problem and with it the estimation errors. We apply our strategy to various datasets and show that it greatly improves the performance of mean-semivariance optimization largely closing the gap in out-of-sample performance with the strategies based on the covariance matrix.
Authors
Andrea Rigamonti and Katarína Lučivjanská Andrea Rigamonti: University of Liechtenstein Katarína Lučivjanská: Pavol Jozef Šafárik University
Keywords
Semivariance ; Principal component analysis ; Minimum average partial ; Parameter uncertainty ; Portfolio optimization ; Downside risk (search for similar items in EconPapers)
Rank
0.74
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Portfolio Optimization
Series
Annals of Operations Research 2024 vol. 334 issue 1 No 8 185-203
Time Added
2024/03/18 03:35
Total Downloads
0
Year Published
2024
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