Time-Series Momentum: A Monte-Carlo Approach

Enoch Cheng and Clemens C. Struck

No 201906, Working Papers from School of Economics, University College Dublin

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.

Keywords: Monte-Carlo; Extreme Value Theory; Backtesting; Risk premia; Time-Series Momentum (search for similar items in EconPapers)
JEL-codes: C12 C52 F37 G12 (search for similar items in EconPapers)
Date: 2019-03
New Economics Papers: this item is included in nep-cmp, nep-ore and nep-rmg
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http://hdl.handle.net/10197/10633 First version, 2019 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:ucn:wpaper:201906

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