Portfolio value-at-risk optimization for asymmetrically distributed asset returns

• Main Authors: GOH, Joel Weiqiang, LIM, Kian Guan, SIM, Melvyn, ZHANG, Weina
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: Risk management; Asymmetric distributions; Partitioned value-at-risk; Portfolio optimization; Robust risk measures; Finance and Financial Management
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/3241; https://ink.library.smu.edu.sg/context/lkcsb_research/article/4240/viewcontent/PortfolioValue_at_risk_2012_afv.pdf
• Institution: Singapore Management University

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.