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 approa...
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Main Authors: | , , , |
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Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2012
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Subjects: | |
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 |
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Institution: | Singapore Management University |
Language: | English |
Summary: | 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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