An Empirical Analysis of the Ross Recovery Theorem

Abstract

Building on the results of Ludwig (2012), we propose a method to construct robust time-homogeneous Markov chains that capture the risk-neutral transition of state prices from current snapshots of option prices on the S&P 500 index. Using the recovery theorem of Ross (2013), we then derive the market’s forecast of the real-world return density and investigate the predictive information content of its moments. We find that changes in the recovered moments can be used to time the index, yielding strategies that not only outperform the market, but are also significantly less volatile.