Time-Series Momentum: A Monte-Carlo Approach
44 Pages Posted: 5 Apr 2019 Last revised: 8 Jun 2019
Date Written: April 4, 2019
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 sizable tail risks, ii) under-perform or outperform. Our results are robust to using different time-series models, time periods, asset classes, and risk measures.
Keywords: Monte-Carlo, Extreme Value Theory, Backtesting, Risk Premia, Time-Series Momentum
JEL Classification: C12, C52, G12, F37
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