Kernel density estimation based distributionally robust mean-CVaR portfolio optimization

Wei Liu (), Li Yang () and Bo Yu ()
Additional contact information
Wei Liu: Beijing Normal University
Li Yang: Dalian University of Technology
Bo Yu: Dalian University of Technology

Journal of Global Optimization, 2022, vol. 84, issue 4, No 10, 1053-1077

Abstract: Abstract In this paper, by using weighted kernel density estimation (KDE) to approximate the continuous probability density function (PDF) of the portfolio loss, and to compute the corresponding approximated Conditional Value-at-Risk (CVaR), a KDE-based distributionally robust mean-CVaR portfolio optimization model is investigated. Its distributional uncertainty set (DUS) is defined indirectly by imposing the constraint on the weights in weighted KDE in terms of $$\phi $$ ϕ -divergence function in order that the corresponding infinite-dimensional space of PDF is converted into the finite-dimensional space on the weights. This makes the corresponding distributionally robust optimization (DRO) problem computationally tractable. We also prove that the optimal value and solution set of the KDE-based DRO problem converge to those of the portfolio optimization problem under the true distribution. Primary empirical test results show that the proposed model is meaningful.

Keywords: Portfolio optimization; Distributionally robust optimization; Kernel density estimation; CVaR; 90B50; 90C15; 90C25; 90C90 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10898-022-01177-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:jglopt:v:84:y:2022:i:4:d:10.1007_s10898-022-01177-5

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/10898

DOI: 10.1007/s10898-022-01177-5

Access Statistics for this article

Journal of Global Optimization is currently edited by Sergiy Butenko

More articles in Journal of Global Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().