Portfolio value-at-risk optimization for asymmetrically distributed asset returns |
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| Authors: | Joel Weiqiang Goh Kian Guan Lim Melvyn Sim Weina Zhang |
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| Institution: | 1. Department of Decision Sciences, NUS Business School, National University of Singapore, Singapore 119245, Singapore;2. Finance and Quantitative Finance Unit, Singapore Management University, Singapore 178899, Singapore;3. Department of Decision Sciences, NUS Business School, National University of Singapore, Affiliated with NUS Risk Management Institute and Singapore-MIT Alliance, Singapore 119245, Singapore;4. Department of Finance, NUS Business School, National University of Singapore, Affiliated with NUS Risk Management Institute, Singapore 119245, Singapore |
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| 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. |
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| Keywords: | Risk management Asymmetric distributions Partitioned value-at-risk Portfolio optimization Robust risk measures |
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