# A volatility filter using historical vol

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We have been looking at a way to improve risk adjusted returns by using a volatility filter. Although we could use VIX or equivalent, it turns out that historical volatility will work just as well, if not a little better.

You can see part 1 here Digging into the VIX, and part 2 here What can we use VIX for?

Although the mean return of how we slice things is zero, the distribution of returns is wider for higher readings of our relative measure of volatility. High volatility begets high volatility, at least for our purposes.

By staying out during periods of higher relative volatility, we aim to reduce drawdowns and the volatility of our returns, leading to better risk adjusted results.

A plus of using HV over some external measure like VIX is that it is readily available for any underlying. This means such a filtering technique can be applied to whatever it is we are trading.

### Performance

Below is a table with two comparisons, the first compares the HV filter to buy and hold. Although performance is generally better, we still get some pretty big drawdowns.

The second adds in a 200 period moving average, which is a reasonably strong way of protecting against downside. Again we can see lower volatility and smaller drawdowns with the addition of a vol filter.

I used a 3 month/63 day look back for our relative volatility measure. I haven’t really dug in to what happens when volatility remains elevated for extended periods of time.

I also did not experiment much with the threshold for where we draw the line on ‘high’ relative volatility. I use 0.6 as the cutoff because I originally split things into quintiles when making the first charts.

I also ran this for RUT and NDX over the same period

These results are all frictionless, don’t factor in dividends, return on cash, etc, etc. I don’t consider this viable as a standalone indicator, but something that can be used along side other factors like rotational strategies, or as a potential tool if you are looking for lower volatility.

Thanks for reading, ’till next time.

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