Article: A new higher moment portfolio optimisation model with conditional value at risk Journal: International Journal of Operational Research (IJOR) 2014 Vol.21 No.4 pp.451 - 465 Abstract: Variance is a common risk measure used for constructing portfolios. However, variance strictly depends on the assumptions that the returns of assets are not normally distributed or investor's function is quadratic. Moreover, variance not only penalises the downside deviations below the mean return but also the upside deviation, variance, thus, does not match investor's desire to maximise the upside deviation and minimise the downside deviation. The objective of this paper is to propose a new four moment mean-conditional-value-at-risk-skewness-kurtosis model and empirically test the model. In this proposed model, variance is replaced with conditional value at risk as the risk measure. The polynomial goal programming method is used in this study as it is flexible to incorporate different degree of investor's preference on mean, skewness and kurtosis. Results of this study demonstrate that the mean-CVaR-skewness-kurtosis model gives higher mean return and skewness and provides better performance than the mean-variance-skewness-kurtosis model for all combinations of degree of preferences. This implies that CVaR is a better risk measure than variance in portfolio optimisation. Inderscience Publishers - linking academia, business and industry through research

Title: A new higher moment portfolio optimisation model with conditional value at risk

Authors: Saiful Hafizah Jaaman; Weng Hoe Lam; Zaidi Isa

Addresses: School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia ' School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia ' School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia

Abstract: Variance is a common risk measure used for constructing portfolios. However, variance strictly depends on the assumptions that the returns of assets are not normally distributed or investor's function is quadratic. Moreover, variance not only penalises the downside deviations below the mean return but also the upside deviation, variance, thus, does not match investor's desire to maximise the upside deviation and minimise the downside deviation. The objective of this paper is to propose a new four moment mean-conditional-value-at-risk-skewness-kurtosis model and empirically test the model. In this proposed model, variance is replaced with conditional value at risk as the risk measure. The polynomial goal programming method is used in this study as it is flexible to incorporate different degree of investor's preference on mean, skewness and kurtosis. Results of this study demonstrate that the mean-CVaR-skewness-kurtosis model gives higher mean return and skewness and provides better performance than the mean-variance-skewness-kurtosis model for all combinations of degree of preferences. This implies that CVaR is a better risk measure than variance in portfolio optimisation.

Keywords: conditional VAR; value at risk; CVaR; kurtosis; mean; polynomial goal programming; PGP; portfolio optimisation; skewness; variance.

DOI: 10.1504/IJOR.2014.065613

International Journal of Operational Research, 2014 Vol.21 No.4, pp.451 - 465

Received: 23 Jul 2012
Accepted: 02 Mar 2013
Published online: 25 Nov 2014 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: A new higher moment portfolio optimisation model with conditional value at risk

Keep up-to-date