Publication Type
Journal Article
Version
acceptedVersion
Publication Date
9-2012
Abstract
We propose a new approach to portfolio optimization by separating asset return distributions into positive and negative half-spaces. The approach minimizes a newly-defined Partitioned Value-at-Risk (PVaR) risk measure by using half-space statistical information. Using simulated data, the PVaR approach always generates better risk-return tradeoffs in the optimal portfolios when compared to traditional Markowitz mean-variance approach. When using real financial data, our approach also outperforms the Markowitz approach in the risk-return tradeoff. Given that the PVaR measure is also a robust risk measure, our new approach can be very useful for optimal portfolio allocations when asset return distributions are asymmetrical.
Keywords
Risk management, Asymmetric distributions, Partitioned value-at-risk, Portfolio optimization, Robust risk measures
Discipline
Finance and Financial Management
Research Areas
Quantitative Finance
Publication
European Journal of Operational Research
Volume
221
Issue
2
First Page
397
Last Page
406
ISSN
0377-2217
Identifier
10.1016/j.ejor.2012.03.012
Publisher
Elsevier
Citation
GOH, Joel Weiqiang; LIM, Kian Guan; SIM, Melvyn; and ZHANG, Weina.
Portfolio value-at-risk optimization for asymmetrically distributed asset returns. (2012). European Journal of Operational Research. 221, (2), 397-406.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/3241
Copyright Owner and License
Authors
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.2012.03.012