Portfolio Value-at-Risk Optimization for Asymmetrically Distributed Asset Returns
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.
Risk management, Asymmetric distributions, Partitioned value-at-risk, Portfolio optimization, Robust risk measures
Finance and Financial Management
European Journal of Operational Research
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. Research Collection Lee Kong Chian School Of Business.
Available at: http://ink.library.smu.edu.sg/lkcsb_research/3241