Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/11004
Title: Portfolio selection based on mean-generalized variance analysis: Evidence from the G20 stock markets
Authors: Li, Zongxin 
Hui, Yongchang 
Prof. WONG Wing-keung 
Lin, Ruiyue 
Issue Date: 2025
Source: Asia-Pacific Journal of Operational Research, 2025, vol. 42(3), article no. 2450016.
Journal: Asia-Pacific Journal of Operational Research 
Abstract: Modern finance theories have been increasingly paying attention to nonlinear and asymmetric features of stock returns. In this paper, we extend the concept of covariance to generalized covariance by using Generalized Measure of Correlation (GMC). Based on the generalized covariance which is capable of catching the nonlinearity and asymmetry in stock (index) returns, we propose a mean-generalized variance portfolio selection model which considers the gross-exposure constraint. Furthermore, we propose the corresponding nonparametric estimation approach and the global optimization algorithm to enhance the applicability of our new model. Empirical studies on G20 stock markets support that a portfolio considering nonlinear and asymmetric features among the international markets would outperform traditional ones based on mean-variance optimization and equal weighting strategy in terms of return and flexibility.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/11004
ISSN: 0217-5959
1793-7019
DOI: 10.1142/S0217595924500167
Appears in Collections:Economics and Finance - Publication

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