Efficient skewness/semivariance portfolios

Abstract

This article proposes a flexible methodology for portfolio selection using a skewness/semivariance biobjective optimisation framework. The solutions of this biobjective optimisation problem allow the investor to analyse the efficient trade-off between skewness and semivariance. This methodology is used empirically on four data sets, collected from the Fama/French data library. The out-of-sample performance of the skewness/semivariance model was assessed by choosing three portfolios belonging to each in-sample Pareto frontier and measuring their performance in terms of skewness per semivariance ratio, Sharpe ratio and Sortino ratio. Both the in-sample and the out-of-sample performance analyses were conducted using three different target returns for the semivariance computations. The results show that the efficient skewness/semivariance portfolios are consistently competitive when compared with several benchmark portfolios.

Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review

“…Alimi et al (2012) used fuzzy programming technology to solve multi-objective fuzzy mean semi-variance portfolio optimization model. Brito et al (2016) proposed a flexible approach to portfolio selection using skewness/semi-variance bio-objective optimization framework, which allowed investors to analyze the effective balance between biases and semi-variables. Salah et al (2016) noted that estimating portfolio risk by conditional variance or conditional semi-variance could obtain information about the future development of different asset returns and help investors to obtain more effective portfolio.…”