Memory-enhanced momentum in commodity futures markets
针对传统动量策略在商品期货市场表现下滑，本文提出通过方差比和赫斯特指数衡量资产记忆性，筛选出可能持续的趋势品种，构建的记忆增强动量策略在收益和风险上显著优于传统策略，且能避免动量崩溃。
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.