Update: added comparison to SPY-COMP.
This post introduces a quant trading model based on volatility. More specifically it uses the prices of volatility futures contracts based on the SP500 to make risk-on and risk-off decisions that can be used to trade various risk-assets.
There is a bunch of research that shows that historical volatility is predictive of future near-term volatility. See here for a good summary of that research. In other words, volatility tends to cluster, low volatility tends to lead to low volatility and higher volatility is associated with higher volatility in the near future. You can think of this as volatility having some of the same characteristics of momentum. Risk-assets, like stocks, exhibit a negative relationship between returns and volatility. Low volatility, higher returns, higher volatility, lower returns. In the quant world these traits have been used for a while in volatility targeting and volatility scaling strategies. Even in the tactical asset allocation world, strategies like Adaptive Asset Allocation (which you can track at Allocate Smartly ) from the Resolve team make use of volatility targeting to improve risk-adjusted returns.
At the other end of the quant world, the short-term, trading oriented side, volatility, in particular volatility futures contracts have been used for a while to actively trade various inverse volatility ETFs, like the now infamous XIV. See here for Volatility Made Simple’s approach and here for QuantStratTradeR’s approach to trading inverse vol ETFs. Both have subscription options if those approaches interest you.
In my approach I use the volatility futures contracts to generate a simple risk-on, risk-off signal for trading a broad range of US stock ETFs, that trades more like a longer term TAA model rather than a short-term trading model.
The Volatility Signal
Like some of the high frequency trading strategies, I use the pricing of short term VIX futures contracts relative to longer term VIX futures contracts to generate a risk-on, risk-off signal. Without giving away the goods, basically, when the short term VIX prices rise above the longer term VIX prices the model goes risk-off. And vice versa, when the VIX prices fall back below longer term VIX prices the model goes back to risk-on. Chart 1 below shows an example of what I’m talking about. It plots the ratio of short-term volatility, as measured by the $VIX index, to the longer-term volatility, as measured by the $VXMT index over the last 5 years. The red line at 1, represents the level where short-term volatility is greater than longer term volatility, i.e. volatility is in backwardation. This would represent a risk-off signal. Similarly, when short-term volatility falls back below some level relative to longer term volatility the model goes back to risk-on mode.
The Volatility Curve Model
Let’s start by looking at the risk-on risk-off signal history of the model. Data goes back to April 2008. Chart 2 below shows the volatility signal generated by the model; 1 meaning risk-off and 0 meaning risk-on. As you can see from the chart it is possible to generate a pretty stable signal from what looks like a very noisy ratio in Chart 1.
Now let’s look at a simple application of the model. We use the vol model’s risk-on, risk-off decisions to trade between SPY and TLT. The results from April 2008 are shown in table 1 below. From April 2, 2008 to December 19, 2009 the SPY/TLT model returned 17.91% with a max daily drawdown of -18.6%. The results compare to a SPY buy and hold return over the same period of 9.76% with a -51.5% drawdown.
Some very nice risk-adjusted returns but what about trading? I wanted a model that did not trade like a high frequency model. Table 2 shows all of the trades since April 2008 for the SPY/TLT model. Basically, there have been 21 trades in 11.5 years. That is not a high number and comparable with many monthly TAA models.
Applications to other risk-assets (stocks, ETFs, bonds)
Of course, the volatility model can be applied to trade many risk-assets. Table 3 below is my tracking sheet for the volatility model where I keep track of several implementations of the model. Results are as of Dec 19, 2019. I also have various benchmarks listed. In every case you get a better risk reward profile than the buy and hold option.
As you can see, due to the quick reaction of the volatility signal, the model can be used quite effectively to trade a very leveraged ETF portfolio (TQQQ/TMF model as an extreme example) depending in your risk tolerance. Also, it can be used in a more conservative framework to even trade risky bonds (the HYG/TLT model) with improved results.
Using the volatility futures curve to generate a risk-on risk-off signal for risk assets generates some impressive risk-adjusted returns. Also, despite being a strategy that requires daily monitoring the amount of trading and turnover is not excessive. I’ve been using the model throughout this year and it has also been in beta testing by a few early subscribers. It will be available as part of the Quant Pulse service by the end of this year. I’ll make a separate announcement when it is available.
Update: below is a comparison of the Vol Curve Model signals to the SPY-COMP signals from my Economic Pulse Newsletter over the same period of time, i.e. from April 2, 2008. Reminder, SPY-COMP is a monthly model. It was risk off until the end of April 2009. The other two risk off months were June 2010, and August 2011.