Robust portfolio selection based on asymmetric measures of variability of stock returns
type: Item
instance of: scholarly article
title: Robust portfolio selection based on asymmetric measures of variability of stock returns
zbMATH Open document ID: 1181.91290
author: Wei Chen
author: Shaohua Tan
published in: Journal of Computational and Applied Mathematics
publication date: 2009-09-29T00:00:00Z
review text: Since asset expected returns and their covariances are often measured with errors, and because of this the resulting solutions to the Markowitz model of portfolio selection are very sensitive to these measurement perturbations, there has been a need to device a new approach which will take into account the estimation errors. This paper proposes a new uncertainty set with randomly fluctuating bounds \(\xi_i= [u_i- \theta^1_i, u_i+\theta^2_i]\) instead of earlier studied ``unknown-but-bounded'' uncertain sets. This new form of the uncertainty set is able to capture asymmetric downside and upside deviations of real world data. The authors apply their interval chance-constrained programming to find robust solution to the mean-variance portfolio selection. A hybrid-intelligent algorithm is employed for this purpose. The numerical experiments suggest that the worst-case behavior of portfolios can be improved using the proposed methodology. The robustness is achieved at low cost.
Mathematics Subject Classification ID: 91G10
Mathematics Subject Classification ID: 90C90
zbMATH DE Number: 5608827
zbMATH Keywords: robust portfolio selection
zbMATH Keywords: interval random uncertainty set
zbMATH Keywords: chance-constrained programming
reviewed by: Leszek S. Zaremba
MaRDI profile type: Publication
full work available at URL: j.cam.2009.06.010
OpenAlex ID: W1995503290
cites work: Robust convex optimization
cites work: Robust Solutions to Least-Squares Problems with Uncertain Data
cites work: Robust Portfolio Selection Problems
cites work: An interior-point method for a class of saddle-point problems
cites work: Robust solutions of linear programming problems contaminated with uncertain data
cites work: Robust asset allocation
DOI: 10.1016/J.CAM.2009.06.010
description: scientific article; zbMATH DE number 5608827