Publication Type

Journal Article

Version

Postprint

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

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org/10.1016/j.ejor.2012.03.012

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