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Abstract
In this article the authors develop a Monte Carlo backtesting procedure for risk premium strategies and employ it to study time-series momentum (TSM). Relying on time-series models, empirical residual distributions, and copulas, the authors address two key drawbacks of conventional backtesting procedures. They 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 that shows that strategies that outperform buy-and-hold in-sample using historical backtests may (1) exhibit tail risks and (2) underperform or outperform when out-of-sample. The results are robust to using different time-series models, time periods, asset classes, and risk measures.
TOPICS: Statistical methods, simulations, big data/machine learning
Key Findings
• Historical backtests suffer from the problem that they contain few tail events which may be an important driver for the performance of risk premium strategies.
• We develop a Monte-Carlo procedure that uses a combination of copulas, time-series models, and empirical residual distributions to overcome this problem.
• Applied to time-series momentum, we find that this strategy may (1) exhibit tail risks (2) underperform or outperform in the long run.
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US and Overseas: +1 646-931-9045
UK: 0207 139 1600