Portfolio Optimization Using Forward-Looking Information

Abstract

We develop a new family of estimators of the covariance matrix that relies solely on forward-looking information. It uses only current prices of plain-vanilla options. In an out-of-sample study, we show that a minimum variance strategy based on these fully-implied estimators outperforms several benchmark strategies, including various strategies based on historical estimates, index investing, and 1/N investing. The outperformance originates in crisis periods when information flow and information asymmetry are high. Although the historical benchmark strategies improve when more recent data are used, they never outperform fully-implied strategies. Thus, our results suggest that investors are better off relying on forward-looking information.