The empirical performance of option implied volatility surface-driven optimal portfolios

We apply a two-step strategy to forecast the dynamics of the volatility surface implicit in option prices to all American-style options written on the stocks that have entered the Dow Jones Industrial Average Index between 2004 and 2016. We explore whether the implied volatilities extracted through the two-step approach help improve the out-of-sample performance of minimum-variance portfolios. We find that, by using option-implied volatilities in estimating the covariance matrix, the ex-post volatility of the minimum-variance portfolio is lower when compared with the equal-weighted portfolio and a minimum-variance portfolio simply derived from the historical, sample covariance matrix estimator. Moreover, over most of our 13-year sample, the realized Sharpe, Sortino and information ratios increase when the sample covariance matrix estimator is replaced with its implied counterpart. However, the benefits of using option-implied information are countered by an increase in portfolio turnover that may imply higher (implicit) transaction costs. We also apply shrinkage methods to both the sample covariance estimator and the implied covariance estimator and note that they often lead to significant improvements in portfolio performance.