... indeed 18 observations is a long.... long... way from anything close to a statistically significant sample size. (my response to random dude)
You can't move on the internet these days for scare stories about the incredibly low level of the VIX, a measure of US implied stock market volatility. Notably the VIX closed below 10 on a couple of days last week, although it has since slightly ticked up. Levels of the VIX this low are very rare - they've only happened on 11 days since 1990 (as of the date I'm writing this).
|The VIX in all it's glory|
The message is that we should be very worried about this. The logic is simple - "Calm before a storm". Low levels of the VIX seem to presage scary stuff happening in the near future. Really low levels, then, must mean a very bad storm indeed.
Consider for example the VIX in early 2007:
This happened then, therefore it will happen again.
It struck me that this story is an example of what behavioural finance type people call narrative bias; the tendency of human beings to extrapolate single events into a pattern. But we need to use some actual statistics to see if we can really extend this anecdotal evidence into a full blown forecasting rule.
There has been some sensible attempt to properly quantify how worried we should be, most notably here on the FT alphaville site, but I thought it worth doing my own little analysis on the subject. Spoiler alert for the terminally lazy: there is probably nothing to be worried about. If you're going to read the rest of the post then along the way you'll also learn a little about judging uncertainty when forecasting, the effect of current vol on future price movements, and predicting volatility generally.
(Note: Explanations for the low level of the VIX abound, and self appointed finance "experts" can be found pontificating on this subject. It's also puzzling how the VIX is so low, when apparently serious sized traders are buying options on it in bucket load sized units (this guy thinks he knows why). I won't be dealing with this conundrum here. I'm only concerned about making money. To make money we just need to judge if the level of the VIX really has any predictive power. We probably don't need to know why the VIX is low.)
Does the level of VIX predict stock prices?
If this was an educational piece I'd work up to this conclusion gradually, but as it's clickbait I'll deal with the question everyone wants to know first (fully aware that most people will then stop reading).
This graph shows the distribution of rolling 20 business day (about one month) US stock returns since 1997:
(To be precise it's the return of the S&P 500 futures contract since I happened to have that lying around; strictly speaking you'd add LIBOR to these. The S&P data goes back to 1997. I've also done this analysis with actual US stock monthly returns going back to 1990. The results are the same - I'm only using the futures here as I have daily returns which makes for nicer, more granular, plots.)
Important point here: this is an unconditional plot. It tells us how (un)predictable one month stock returns are in the absence of any conditioning information. Now let's add some conditioning information - the level of spot VIX:
- The average return doesn't seem to be any different between the two periods of history
- The blue distribution is wider than the red one. In other words if spot VIX is high, then returns are likely to be more volatile. Really this is just telling us that implied vol (what the VIX is measuring) is a pretty good predictor of realised vol (what actually happens). I'll talk more about predicting vol, rather than the direction of returns, later in the post.
- Digging in a bit more it looks like there are more bad returns in the blue period (negative skew to use the jargon)
The upshot of the first bullet point is that spot VIX doesn't predict future equity returns very well. In fact the average monthly return is 0.22% when vol is low, and 0.38% when vol is high; a difference of 0.16% a month. That doesn't seem like a big difference - and it's hard to see from the plot - but can we test that properly?
Yes we can. This plot shows the distribution of the differences in averages:
A negative number here means that high VIX gives a higher return than low VIX. We already know this is true, but the distribution plot shows us that this difference is actually reasonably significant. In fact 94.4% of the differences above are below zero. That isn't quite at the 95% level that many statisticians use for significance testing, but it's close.
To put it another way we can be 94.4% confident that the expected return for a low VIX (below 20%) environment will be lower than that for days when VIX is high (above 20%).
A moments thought shows it would be surprising if we got a different result. In finance we expect that with a higher return you will get higher risk. We know that when VIX is high that returns will have a higher volatility. So it's not shocking that they also have higher risk.
So a better way of testing this is to use risk adjusted returns. This isn't the place to debate the best way of risk adjusting returns, I'm going to use the Sharpe Ratio and that is that. Here I define the Sharpe as the 20 business day return divided by the volatility of that return, and then annualised.
Now we've adjusted for risk there is little to choose between the high VIX and low VIX environments. In fact things have reversed, with low VIX having a higher Sharpe Ratio than high VIX. But the difference in Sharpes is just 0.04, which isn't very much.
We can only be 63% confident that low VIX is better than high VIX. This is little better than chance, which would be 50% confidence.
An important point: notice that although the difference in Sharpes isn't significant, we do know it with reasonably high confidence, as each bucket of observations (high or low VIX) is quite large. We can be almost 100% confident that the difference was somewhere between -0.04 and +0.04.
"Hang on a minute!", I hear you cry. The point now is that vol is really really low now. The analysis above is for VIX above and below 20%. You want to know what happens to stock returns when VIX is incredibly low - below 10%.
The conditional Sharpe Ratio for VIX below 10 is actually negative (-0.14) versus the positive Sharpe we get the rest of the time (0.14). Do we have a newspaper story here?
Here is the plot of Sharpe Ratios for very low VIX below 10% (red), and the rest of the time (blue):
Here for example is the plot of the difference between the Sharpe Ratio of returns for very low VIX and 'normal' VIX.
Perhaps we should do a "proper" quant investingation, and take the top and bottom 10% of VIX observations, plus the middle, and compare and contrast.That way we can get some more data. After all although statistics can allow us to make inferences from tiny sample sizes (like the 11 days the VIX closed below 10), it doesn't mean we should.
The big blue area is obviously the middle of the VIX distribution; whilst the purple (actually red on blue) is relatively low VIX, and the green is relatively high VIX.
It's not obvious from the plot but there is actually a nice pattern here. When the VIX is very low the average SR is 0.071; when it's in the middle the SR is 0.139, and when it's really high the SR is 0.20.
Comparing these numbers the differences are actually highly significant (99.3% chance mid VIX is better than low VIX, 98.4% chance high VIX is better than mid VIX, and 99.999% chance high VIX is better than low VIX).
So it looks like there might be something here - an inverse relationship between VIX and future equity returns. However to be clear you should still expect to make money owning S&P 500 when the VIX is relatively low - just a little bit less money than normal. Buying equities when the VIX is above 30 also looks like a good strategy. It will be interesting to see if market talking heads start pontificating on that idea when, at some point, the VIX gets back to that level.
"Hang on another minute!!", I hear you unoriginally cry, again. The original story I told at the top of this post was about VIX spiking in February 2007, and the stock market reacting about 18 months later. Perhaps 20 business days is just too short a period to pick up the effect we're expecting. Let's use a year instead.
I could play with permutations of these figures all day, and I'd be rightly accused of data mining. So let me summarise. Buying when the VIX is really high (say above 30) will probably result in you doing well, but you'll need nerves of steel to do it. Buying when the VIX is really low (say less than 15) might give you results that are a little worse than usual, or they might not.
However there is nothing special about the VIX being below 10. We just can't extrapolate from the tiny number of times it has happened and say anything concrete.
Does the level of VIX predict vol?
Whilst the VIX isn't that great for predicting the direction of equity markets, I noted in passing above that it looks like it's pretty good at predicting their future volatility.
We're still conditioning on low, middling, and high VIX here but the response variable is the annualised level of volatility over the subsequent 20 days. You can see that most of the red (turning purple) low VIX observations are on the left hand side of the plot - low VIX means vol will continue to be low. The green (high VIX) observations are spread out over a wider area, but they extend over to the far right.
Low VIX (below 12.5): Average subsequent vol 8%
Medium VIX: Average subsequent vol 12.3%
High VIX: Average subsequent vol 21.9%
These numbers are massively statistically significant from each other (above 99.99%). I get similar numbers for trying to predict one year volatility.
So it looks like the current low level of VIX means that prices probably won't move very much.
Does the level of vol predict vol?
The VIX is a forward looking measure of future volatility, and it turns out a pretty good one. However there is an even simpler predictor of future vol, and that is recent vol. The level of the VIX, and the level of recent volatility, are very similar - their correlation is around 0.77.
Skipping to the figures, how well does recent vol (over the last 20 days) predict subsequent vol (over the next 20 days)?
Recent Vol less than 6.7%: Average subsequent vol 7.9%
Recent Vol between 6.7% and 21.7%: Average subsequent vol 13.2%
Recent Vol over 21.7%: Average subsequent vol 23.4%
These are also hugely significant differences (>99.99% probability).
The best way of predicting volatility
Interestingly if you use the VIX to try and predict what the VIX will be in one months time you find it is also very good. Basically both recent vol and implied vol (as measured by the VIX) cluster - high values tend to follow high values, and vice versa. Over the longer run vol tends not to stay high, but will mean revert to more average levels - and this applies to both implied vol (so the VIX) and realised vol.
So a complete model for forecasting future volatility should include the following:
- recent vol (+ve effect)
- current implied vol (the VIX) (+ve)
- recent vol relative to long run average (-ve)
- recent level of spot VIX relative to long run average
- (You can chuck in intraday returns and option smile if you have time on your hands)
However there is decreasing benefit from including each of these things. Recent vol does a great job of telling you what vol is probably going to be in the near future. Including the current level of the VIX improves your predictive power, but not very much.
The importance of the VIX to future equity returns is somewhat overblown. It's just plain silly to say we can forecast anything from something that's only happened on a handful of occasions in the past (granted that the handful in question belongs to someone with 11 fingers). Low VIX might be a signal that returns will be a little lower than average in the short term, but by no means is inevitable impending doom fast approaching.
If there is a consistent lesson here it's that very high levels of VIX are a great buy signal.
The VIX is also helpful for predicting future volatility - but if you have room in your life for just one forecasting rule using recent realised vol is better.