Time-Series Momentum: A Monte-Carlo Approach

Abstract

This paper develops a Monte-Carlo backtesting procedure for risk premia strategies and employs it to study Time-Series Momentum (TSM). Relying on time-series models, empirical residual distributions and copulas we overcome two key drawbacks of conventional backtesting procedures. We create 10,000 paths of different TSM strategies based on the S&P 500 and a cross-asset class futures portfolio. The simulations reveal a probability distribution which shows that strategies that outperform Buy-and-Hold in-sample using historical backtests may out-of-sample i) exhibit sizeable tail risks ii) underperform or outperform. Our results are robust to using different time-series models, time periods, asset classes, and risk measures.