Time-Series Momentum: A Monte-Carlo Approach
|Title:||Time-Series Momentum: A Monte-Carlo Approach||Authors:||Cheng, Enoch; Struck, 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-Carlo; Extreme Value Theory; Backtesting; Risk premia; Time-Series Momentum||Language:||en||Status of Item:||Not peer reviewed|
|Appears in Collections:||Economics Working Papers & Policy Papers|
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.