Multi-objective portfolio optimization under tempered stable Lévy distribution with Copula dependence

Xiao-Li Gong and Xiong Xiong

Finance Research Letters, 2021, vol. 38, issue C

Abstract: Taking into account the leptokurtosis nature of financial returns distribution and the non-linear dependence structure of the underlying assets variables in portfolio, the tempered stable Lévy distribution and the Copula function (TS Copula) are employed to describe the multi-objective portfolio optimization problem. In order to investigate the modeling ability of TS distribution coupling with different Copula functions, the model is designed to maximize the benefits while minimizing the risk in finding a set of non-dominant Pareto solutions. The problem of constrained TS Copula multi-objective investment optimization is solved by using three intelligent algorithms, namely the NSGA-II, SPEA-II and MOPSO. Then the empirical studies in Chinese stock markets illustrate that the returns distribution is leptokurtic and heavy tailed. Furthermore, the Skewed-t Copula function coupling with tempered stable marginal distribution can effectively capture the thick tail distribution of portfolio returns and the non-linear asymmetric dependence structure among assets. It is the Skewed-t Copula coupling with tempered stable Lévy distribution that gets the best fitting performance. In addition, the MOPSO and NSGA-II intelligent algorithms are effective in solving TS Copula based multi-objective portfolio optimization.

Keywords: Leptokurtosis; Copula dependence; Multi-objective portfolio (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1544612320301343
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:38:y:2021:i:c:s1544612320301343

DOI: 10.1016/j.frl.2020.101506

Access Statistics for this article

Finance Research Letters is currently edited by R. Gençay

More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().