Time series momentum :
a Monte Carlo approach /
Enoch Cheng, University of Colorado, Clemens Struck, University College Dublin.
| Main Creator: | |
|---|---|
| Contributors: | |
| Summary: | This paper develops a Monte-Carlo backtesting procedure for risk premia strategies andemploys it to study Time-Series Momentum (TSM). Relying on time-series models, empiricalresidual distributions and copulas we overcome two key drawbacks of conventional backtestingprocedures. We create 10,000 paths of different TSM strategies based on the S & P 500 and across-asset class futures portfolio. The simulations reveal a probability distribution which showsthat strategies that outperform Buy-and-Hold in-sample using historical backtests may out-of-sample i) exhibit sizable tail risks ii) underperform or outperform. Our results are robust tousing different time-series models, time periods, asset classes, and risk measures. |
| Format: | Book |
| Language: | English |
| Published / Created: |
Dublin :
UCD School of Economics, University College Dublin,
March 2019.
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| Series: | Working paper (University College, Dublin. Centre for Economic Research) ;
no. 19/06. |
| Subjects: | |
| Notes: | Includes bibliographical references. Physical description: 42 pages : graphs ; 21 cm more |
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| 100 | 1 | |a Cheng, Enoch, |e author. | |
| 245 | 1 | 0 | |a Time series momentum : |b a Monte Carlo approach / |c Enoch Cheng, University of Colorado, Clemens Struck, University College Dublin. |
| 264 | 1 | |a Dublin : |b UCD School of Economics, University College Dublin, |c March 2019. | |
| 300 | |a 42 pages : |b graphs ; |c 21 cm | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a Working paper series / UCD Centre for Economic Research ; |v WP19/06 | |
| 504 | |a Includes bibliographical references. | ||
| 520 | |a This paper develops a Monte-Carlo backtesting procedure for risk premia strategies andemploys it to study Time-Series Momentum (TSM). Relying on time-series models, empiricalresidual distributions and copulas we overcome two key drawbacks of conventional backtestingprocedures. We create 10,000 paths of different TSM strategies based on the S & P 500 and across-asset class futures portfolio. The simulations reveal a probability distribution which showsthat strategies that outperform Buy-and-Hold in-sample using historical backtests may out-of-sample i) exhibit sizable tail risks ii) underperform or outperform. Our results are robust tousing different time-series models, time periods, asset classes, and risk measures. | ||
| 542 | 0 | |g 2019 |h 20?? | |
| 650 | 0 | |a Time-series analysis. | |
| 650 | 0 | |a Extreme value theory. | |
| 650 | 0 | |a Investments |x Risk assessment. | |
| 700 | 1 | |a Struck, Clemens C., |e author. | |
| 710 | 2 | |a University College, Dublin. |b Centre for Economic Research, |e issuing body. | |
| 710 | 2 | |a UCD School of Economics, |e publisher. | |
| 830 | 0 | |a Working paper (University College, Dublin. Centre for Economic Research) ; |v no. 19/06. | |
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