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

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Author Info

  • Goh, Joel Weiqiang
  • Lim, Kian Guan
  • Sim, Melvyn
  • Zhang, Weina

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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Bibliographic Info

Article provided by Elsevier in its journal European Journal of Operational Research.

Volume (Year): 221 (2012)
Issue (Month): 2 ()
Pages: 397-406

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Handle: RePEc:eee:ejores:v:221:y:2012:i:2:p:397-406

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Web page: http://www.elsevier.com/locate/eor

Related research

Keywords: Risk management; Asymmetric distributions; Partitioned value-at-risk; Portfolio optimization; Robust risk measures;

References

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Citations

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Cited by:
  1. Wong, Man Hong & Zhang, Shuzhong, 2014. "On distributional robust probability functions and their computations," European Journal of Operational Research, Elsevier, vol. 233(1), pages 23-33.
  2. Gabrel, Virginie & Murat, Cécile & Thiele, Aurélie, 2014. "Recent advances in robust optimization: An overview," European Journal of Operational Research, Elsevier, vol. 235(3), pages 471-483.
  3. Adcock, C.J., 2014. "Mean–variance–skewness efficient surfaces, Stein’s lemma and the multivariate extended skew-Student distribution," European Journal of Operational Research, Elsevier, vol. 234(2), pages 392-401.
  4. Lioui, Abraham & Poncet, Patrice, 2013. "Optimal benchmarking for active portfolio managers," European Journal of Operational Research, Elsevier, vol. 226(2), pages 268-276.

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