Please use this identifier to cite or link to this item:
|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|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jun 19, 2018
WEB OF SCIENCETM
checked on Jun 12, 2018
checked on May 5, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.