Robust Portfolio Optimization with Multi-Factor Stochastic Volatility

Ben-Zhang Yang (), Xiaoping Lu (), Guiyuan Ma () and Song-Ping Zhu ()
Additional contact information
Ben-Zhang Yang: Sichuan University
Xiaoping Lu: University of Wollongong
Guiyuan Ma: The Chinese University of Hong Kong
Song-Ping Zhu: University of Wollongong

Journal of Optimization Theory and Applications, 2020, vol. 186, issue 1, No 14, 264-298

Abstract: Abstract This paper studies a robust portfolio optimization problem under a multi-factor volatility model. We derive optimal strategies analytically under the worst-case scenario with or without derivative trading in complete and incomplete markets and for assets with jump risk. We extend our study to the case with correlated volatility factors and propose an analytical approximation for the robust optimal strategy. To illustrate the effects of ambiguity, we compare our optimal robust strategy with the strategies that ignore the information of uncertainty, and provide the welfare analysis. We also discuss how derivative trading affects the optimal strategies. Finally, numerical experiments are provided to demonstrate the behavior of the optimal strategy and the utility loss.

Keywords: Robust portfolio selection; Multi-factor volatility; Jump risks; Non-affine stochastic volatility; Ambiguity effect; 91B28; 60H30; 91C47; 91B70 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1007/s10957-020-01687-w 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:joptap:v:186:y:2020:i:1:d:10.1007_s10957-020-01687-w

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2

DOI: 10.1007/s10957-020-01687-w

Access Statistics for this article

Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull

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