Trading VIX Futures Using Machine Learning Techniques

VIX futures are financial contracts that allow investors to trade on the expected future volatility of the S&P 500 Index, as measured by the VIX (Volatility Index). These contracts provide a way to hedge against or speculate on changes in market volatility. VIX futures are popular in times of uncertainty, as they tend to increase in value when market volatility spikes, making them useful for managing risk or profiting from volatile market conditions.

Reference [1] proposed using Constant Maturity Futures (CMF) to generate trading signals for VIX futures. It applied seven machine learning models to create these signals. The authors pointed out,

The experiment results indicate that VIX CMFs term structure features, specifically, μt and ∆roll, are highly effective in predicting the next-day returns of VIX CMFs and could potentially yield significant economic benefits. However, statistically derived features possess comparatively less predictive ability. Additionally, the C-MVO strategy shows overall superior backtesting performance across most machine learning models compared to the benchmark rank-based long-short strategy, providing valuable insights and practical implications for the formulation of trading strategies involving VIX CMFs and proving that numerically predicted returns can better guide trading strategies. Finally, evaluations of the machine learning models revealed that within the neural-network-based models, ALSTM exhibited the best performance in both predictive and backtesting assessments. No single tree-based model demonstrated clear superiority. More importantly, the linear regression model, which considers only linear relationships, outperforms all other models, thereby affirming the substantial ability of term structure features in predicting the next-day returns of VIX CMFs.

In short, the authors successfully developed timing strategies by leveraging CMF data and machine learning techniques, with promising results.

We note that this research integrates data science techniques with domain-specific knowledge, and we believe that this combination offers a higher chance of success than using data science methods alone.

Let us know what you think in the comments below or in the discussion forum.

References

[1] Wang S, Li K, Liu Y, Chen Y, Tang X (2024), VIX constant maturity futures trading strategy: A walk-forward machine learning study, PLoS ONE 19(4): e0302289.

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