Portfolio Selection under Distributional Uncertainty: A Relative Robust CVaR in Portfolio Management
Publication Type
Journal Article
Version
submittedVersion
Publication Date
5-2010
Abstract
Robust optimization, one of the most popular topics in the field of optimization and control since the late 1990s, deals with an optimization problem involving uncertain parameters. In this paper, we consider the relative robust conditional value-at-risk portfolio selection problem where the underlying probability distribution of portfolio return is only known to belong to a certain set. Our approach not only takes into account the worst-case scenarios of the uncertain distribution, but also pays attention to the best possible decision with respect to each realization of the distribution. We also illustrate how to construct a robust portfolio with multiple experts (priors) by solving a sequence of linear programs or a second-order cone program.
Keywords
Conditional value-at-risk; Worst-case conditional value-at-risk; Relative robust conditional value-at-risk; Portfolio selection problem; Linear programming
Discipline
Finance and Financial Management | Portfolio and Security Analysis
Research Areas
Finance
Publication
European Journal of Operational Research
Volume
203
Issue
1
First Page
185
Last Page
194
ISSN
0377-2217
Identifier
10.1016/j.ejor.2009.07.010
Publisher
Elsevier
Citation
Dashan HUANG; ZHU, Shushang; FABOZZI, Frank; and FUKUSHIMA, Masao.
Portfolio Selection under Distributional Uncertainty: A Relative Robust CVaR in Portfolio Management. (2010). European Journal of Operational Research. 203, (1), 185-194.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/4782
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.ejor.2009.07.010