There is a view that managed futures is a long volatility strategy or at least should do well in higher volatility. Some researchers have described managed futures as being long a straddle, but a simple examination of the data suggests that the relationship between managed futures returns and volatility is more complex.
I ran a simple test across monthly and daily data over the same time frame between the leading managed futures indices and the VIX volatility index. For the period January 2000 until September 2015, I matched the managed futures performance with the VIX index. I compared the average daily and monthly returns for the top 20% and bottom 20% of the sample against the mean return for the entire sample. This is a very simple test and may only be suggestive of the results, but it did cause me to have think more closely about the link between managed futures and volatility.
If you believe that managed futures is a long volatility strategy, the average returns for the top 20% of volatility days and months should be higher than the overall average and certainly higher than the lowest 20% of the sample. For the BTOP50 index, the results show that the average return for the top 20% of the volatility months were higher than the entire sample by a sizable amount. However, it is interesting that the average return for the bottom 20% of the months also did well. It could be that managers lever up in low volatility periods, but these results do not seem obvious. For the daily data using the SocGen CTA index, the results are reversed. The average return for the bottom 20% volatility days is actually higher than the return for the top 20% volatility days. Both do better than the entire data set.
The managed futures managers do not seem to like high daily volatility, but they can exploit high monthly volatility. I view managed futures managers as being long longer-term volatility. This allows them to exploit the spread in prices from higher volatility, but they do not like high short-term volatility. High daily volatility increases the likelihood of being stopped out and the stop-out feature makes positions behave like knock-out options. When the stop price is hit, you loss your position. This can explain what the data suggest; however, a closer inspection should be conducted.