Robust portfolio selection based on asymmetric measures of variability of stock returns

Authors:
Wei Chen;Shaohua Tan
Affiliations:
State Key Laboratory of Machine Perception, Peking University, Beijing 100871, China;State Key Laboratory of Machine Perception, Peking University, Beijing 100871, China
Venue:
Journal of Computational and Applied Mathematics
Year:
2009

Citing 5
Cited 1

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Abstract

This paper addresses a new uncertainty set-interval random uncertainty set for robust optimization. The form of interval random uncertainty set makes it suitable for capturing the downside and upside deviations of real-world data. These deviation measures capture distributional asymmetry and lead to better optimization results. We also apply our interval random chance-constrained programming to robust mean-variance portfolio selection under interval random uncertainty sets in the elements of mean vector and covariance matrix. Numerical experiments with real market data indicate that our approach results in better portfolio performance.