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|Title:||Portfolio value-at-risk optimization for asymmetrically distributed asset returns|
|Authors:||Goh, J.W. |
Robust risk measures
|Citation:||Goh, J.W., Lim, K.G., Sim, M., Zhang, W. (2012). Portfolio value-at-risk optimization for asymmetrically distributed asset returns. European Journal of Operational Research 221 (2) : 397-406. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ejor.2012.03.012|
|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. © 2012 Elsevier B.V. All rights reserved.|
|Source Title:||European Journal of Operational Research|
|Appears in Collections:||Staff Publications|
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