Time-Series Momentum: A Monte-Carlo Approach

44 Pages Posted: 5 Apr 2019 Last revised: 8 Jun 2019

See all articles by Enoch Cheng

Enoch Cheng

University of Colorado at Denver - Department of Economics

Clemens Struck

University College Dublin

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

Suggested Citation

Cheng, Enoch and Struck, Clemens, Time-Series Momentum: A Monte-Carlo Approach (April 4, 2019). Available at SSRN: https://ssrn.com/abstract=3345849 or http://dx.doi.org/10.2139/ssrn.3345849

Enoch Cheng

University of Colorado at Denver - Department of Economics ( email )

Campus Box 181
P.O. Box 173364
Denver, CO 80217-3364
United States

Clemens Struck (Contact Author)

University College Dublin ( email )

School of Economics
Belfield, Dublin 4

HOME PAGE: http://ccstruck.weebly.com

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