Time-Series Momentum: A Monte-Carlo Approach

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Title: Time-Series Momentum: A Monte-Carlo Approach
Authors: Cheng, EnochStruck, Clemens C.
Permanent link: http://hdl.handle.net/10197/10633
Date: Mar-2019
Online since: 2019-05-23T09:30:04Z
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.
Type of material: Working Paper
Publisher: University College Dublin. School of Economics
Series/Report no.: UCD Centre for Economic Research Working Paper Series; WP19/06
Copyright (published version): 2019 the Authors
Keywords: Monte-CarloExtreme Value TheoryBacktestingRisk premiaTime-Series Momentum
Language: en
Status of Item: Not peer reviewed
Appears in Collections:Economics Working Papers & Policy Papers

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