Macro Investor’s VIX Trading Strategy

By reader request, this is a test of a strategy from Macro Investor for trading VIX ETPs like XIV and VXX. This strategy, like many you’ll find on this blog and elsewhere, uses the shape of the VIX futures term-structure, going short the VIX when futures are more contangoed and long when more backwardated.

Strategy results from 07/2004 trading XIV (inverse VIX) and VXX (long VIX) follow. Read about test assumptions, or get help following this strategy.

20141209.01

The strategy rules I’ve tested are a bit different than the ones presented by Macro Investor in order to stay more in line with how we generally test things here at VMS. Strategy rules:

  • At the close, calculate R, where R = [average of (VX1 / VIX, VX2 / VX1) – 1]. VX1 and VX 2 represent first and second month VIX futures, and VIX the VIX spot.
  • At the close, calculate the average value for R from inception up to that moment in time.
  • Go long XIV at the close if R > (average historical R * -1), otherwise go long VXX. Hold until a change in position.

Differences between my test and Macro Investor’s:

  • MI’s test traded UVXY (2x leveraged VIX) in place of VXX. This strategy has only spent about 6% of all days long VIX. That greatly increases the risk of overfitting those particular days. We would compound that impact of overfitting by assuming that we traded a leveraged product. Bear in mind that VIX ETPs are already insanely volatile. It would be bad stewardship on my part to even share such a backtest.
  • After applying the strategy to VIX ETPs, MI went on to then apply the strategy to ETP options. I have not done so here as options are beyond the scope of what we usually discuss at VMS, but note that my comments below would apply to options trading as well.

20141209.02

20141209.03

I think the basic concept behind the strategy, going short the VIX when futures are closer to contangoed and long when closer to backwardation, is a sound one and we’ve tested a number of similar strategies previously, such as the VIX vs Front Month and First vs Second Month strategies.

On paper, Macro Investor’s strategy has performed better than most, with an exceptionally smooth equity curve after the 2007/08 crash, but I think there’s an extra helping of overfitting with this strategy as a result of the introduction of the “average historical R”.

The average R value is based on all data from inception up to date. Note that the strategy doesn’t “peek” into the future as the average R value is recalculated each day in our test using only the data available to us at that moment in time.

But let’s pretend that we did have perfect foresight and could use the most up-to-date average R value (4.8%) for our entire test. In other words, from day 1 until the most recent trading day, average R always equals ~4.8%.

With this perfect foresight, this strategy should to some degree improve if the average R is actually honing in on some optimal value, but in fact it does just the opposite. It goes from a sexy Sharpe/UPI of 1.3/2.7 to a less impressive 1.0/1.9.

That tells me that the way in which the “average R” has changed over time (which is an entirely arbitrary result of the date on which the test started, and shouldn’t in and of itself be predictive), is overfit to this particular moment in history and unlikely to provide value out-of-sample.

Again though, beyond my criticism re: the average R and use of a leveraged long VIX ETP, I think the basic concept behind the strategy does have value.

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A big thank you to Macro Investor for posting this strategy.

When the strategies that we cover on our blog (including this one) signal new trades, we include an alert on the daily report sent to subscribers. This is completely unrelated to our own strategy’s signal; it just serves to add a little color to the daily report and allows subscribers to see what other quantitative strategies are saying about the market.

Click to see Volatility Made Simple’s own elegant solution to the VIX ETP puzzle.

Good Trading,
Volatility Made Simple

Posted in Strategy Backtests.