Portfolio Optimization Using Forward-Looking Information

Abstract

In this paper we develop a new family of estimators of the covariance matrix that relies solely on forward-looking information. These estimators only use current price information from a cross-section of plain-vanilla options and employ different higher moments of the implied return distributions. In an out-of-sample study for US blue-chip stocks we show that a minimum-variance strategy based on these fully implied covariance estimators consistently outperforms a wide range of different benchmark strategies, including strategies based on historical estimates, index investing, and investing according to the 1/N rule. This result is very robust and holds with and without short-sales restrictions, with portfolios being rebalanced at different frequencies, and with transactions costs taken into account. The outperformance is particular strong in crisis periods when information flow and information asymmetry are high. The outperformance can only be reached using a fully implied approach; partially implied approaches that combine implied moments with historical ones might even perform worse than purely historical approaches. We further observe that covariance estimators based on implied second and fourth moments outperform estimators based on implied skewness. In conclusion, our results show that investors can better exploit possible diversification benefits by relying solely on forward-looking information from options markets.

Keywords: portfolio selection, option-implied information

JEL Classification: G11, G13, G17