Using VIX Futures Term Structure for Trading

The volatility index, VIX, is a measure of the stock market’s expectation of volatility over the next 30 days. The VIX is calculated by using the prices of options on the S&P 500 Index. The VIX futures are derivative contracts that allow investors to bet on the future values of the spot VIX.

The VIX futures term structure is the series of VIX futures values for different expiration dates. The term structure can be in either contango or backwardation. Contango is when the VIX futures are higher for contracts with longer expiration dates. Backwardation is when the VIX futures are higher for contracts with shorter expiration dates. Backwardation is the expectation that the VIX index will fall. Contango, however, occurs most of the time due to the mean-reverting nature of volatility.

Reference [1] presented a trading approach based on the VIX index, VIX Futures term structure, S&P500, and NASDAQ100 indices with the aid of a machine learning method called Random Forests. The findings are,

Two different strategies used to evaluate Contango and Backwardation on SPX (S&P500 index) and IXIC (NASDAQ composite index) historical data. Both the strategies provided good accuracy (more than 94.8%) for Contango. In quantitative analysis, strategy 2 is performing well with high close value and cumulative returns…

In addition, a major limitation of Random Forests models consists in the fact that a huge number of trees might cause the algorithm to perform very slowly and inefficiently when making predictions in real time. Generally, such algorithms tend to be fairly fast to train, but relatively slow to generate forecasts.

The novelty in this approach is that the VIX futures term structure was used as a variable to generate trading signals. The authors developed 2 trading strategies that resulted in Sharpe ratios of 2.24 and 2.6 respectively.


[1] Ayoub Bensakhria, Zororo Makumbe, and Govind Savara, Volatility and Risk – Stock Market Returns Based on the State of the VIX Futures Term-Structure, 2022,

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