This paper examines the portfolio optimization of energy
futures by using the STARR ratio that can evaluate the risk and return relationship for skewed distributed returns. We model the price returns for energy
futures by using the ARMA(1,1)- GARCH(1,1)-PCA model with stable distributed innovations that reflects the characteristics of energy: mean reversion, heteroskedasticity, seasonality, and spikes. Then, we propose the method for selecting the portfolio of energy
futures by maximizing the STARR ratio, what we
call "Winner portfolio". The empirical studies by using energy
futures of WTI crude oil, heating oil, and natural gas traded on the NYMEX compare the price return models with stable distributed innovations to those with normal ones. We show that the models with stable ones are more appropriate for energy
futures than those with normal ones. In addition, we discuss what characteristics of energy
futures cause the stable distributed innovations in the returns. Then, we generate the price returns of energy
futures using the ARMA(1,1)-GARCH(1,1)-PCA model with stable ones and choose the portfolio of energy
futures employing the generated price returns. The results suggest that the selected portfolio of "Winner portfolio" perform better than the average weighted portfolio of "Loser portfolio". Finally, we examine the usefulness of the STARR ratio to select the winner portfolio of energy
futures.
[Authors: Almira Biglova, Takashi Kanamura, Svetlozar T. Rachev, Stoyan Stoyanov]