An empirical comparison of different two-factor models in the context of portfolio optimisation

Abstract

The crisis linked to the COVID-19 and the uncertainty it generates in the unprecedented health, societal, economic and financial fields have had a strong impact on the stock markets. Indeed, in such a climate of very high uncertainty, it is to be expected that the excessive stock market price movements will continue, with both declines and technical rebounds, and that the resulting volatility will remain particularly high. In order to cope with this crisis, investors and portfolio managers must mobilize all portfolio selection strategies. In particular, portfolio management and construction are based on the concepts of return and risk. This couple has been at the center of all the concerns of managers and investors in portfolio optimization issues since the introduction of the mean-variance model by Markowitz. However, many studies have proposed different measures of risk to overcome the drawbacks of variance. The objective of this paper is to present and compare the portfolio compositions and performance of four different portfolio optimization models using different risk measures, including variance, Mean Absolute Deviation, Gini coefficient and Lower Partial Moments (LPM). The results of this study show that the Mean-Lower Partial Moments (MLPM) model outperforms other models. The Mean-Lower Partial Moments (MLPM) model is suitable for investors during the crisis period (COVID-19) in the Moroccan financial market.