Trading in a low vol world
[This article was first published on Shifting sands, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
I wanted to take a look at what works in low vol environments, such as we are currently experiencing. I am open to the idea we have entered a period of structurally low volatility due to increased regulatory burden and flow on effects from the decline of institutional FICC trading. Or it may just be a function of QE, and post-tapering we will see a return to higher levels.Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
The plan
I took a look at the major US equity indices, SPX (GSPC), NDX and RUT.
For each series we calculate daily log returns for the current period and shift the forward to get the return for the next period. Then we calculate realized volatility (RV) and split the data set into “low volatility” and “high volatility”, by looking at median realized vol for the whole series.
Then, for each series, we use bootstrapped samples to simulate a number of trajectories/equity curves for each strategy (MR/FT) under the two classes of RV. Finally we take an average of the total return of each trajectory to get a ballpark idea of how they went.
Results
The data is from the start of 1999 to the present, so roughly 15 years. Each run generates 1000 trajectories with a sample size of roughly 950.
For the low vol case, the results are unfortunately ambiguous. Follow through in a low vol environment seemed to do well for NDX and RUT, but the opposite was the case for SPX.
The TR column is the sum of the series over the whole period for the volatility class (i.e. a simple long only strategy), giving an idea of a directional bias that may be present in the sampling.
In the high vol environment, mean reversion was a clear winner, and consistent over the different underlyings.
The results seem relatively stable across trajectory size/sample size.
Outro
I’m not really sure what is going on SPX. My intuition was that FT would do well in low vol environments, but that doesn’t seem to be the case, at least not for SPX.
I was actually getting consistent votes for FT in the low vol case, then restarted R to run with a clean environment and started getting the above instead. You can’t spell argh without R it seems.
Source is up here. As always you can find me on twitter here. Thanks for stopping by.
To leave a comment for the author, please follow the link and comment on their blog: Shifting sands.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.