Memory-enhanced momentum in commodity futures markets

Motivated by the deteriorating performance of traditional cross-sectional momentum strategies in commodity futures markets, we propose to resurrect momentum by incorporating autocorrelation information into the asset selection process. Put differently, we introduce measures of short and long memory (variance ratios and Hurst coefficients, respectively) telling us whether past winners and losers are likely to persist or not. Our empirical findings suggest that a memory-enhanced momentum strategy based on variance ratios significantly outperforms traditional momentum in terms of reward and risk, effectively prevents momentum crashes and is not bound to the movement of the overall commodity market. The strategy returns cannot be explained by typical factor portfolios and macroeconomic variables. They are also robust to alternative data sets, transaction costs and data snooping. In comparison, Hurst coefficients carry less investment-relevant information and cannot outperform variance ratios in terms of risk premia and investment alpha.