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by Carson Dahlberg, CMT
Special to the TradeTech Blog
We’ve all had that gut feeling when seeing high correlations among assets – that it might not be good – but perhaps without having a way to measure its effect. Some try to avoid correlation by holding a portfolio of non-correlated/low-correlated assets. Some try to monitor correlation as a gauge for market risk (the more correlated assets are, the more likely that risk in the market is high). This is an insight that does seem to make sense – I don’t need to add to all that’s been written about how when market panics occur all assets basically head to a correlation of 1.
What happens when correlations remain elevated for significantly long periods of time? This is a timely question for the S&P500 Index. In our business, it is not possible to sit on the sidelines, at least not for long. Is there additional actionable info to pull out of the concept of correlation other than “stay alert?”
One of my mantras is “add precision to insight” and I will do just that using the CBOE S&P500 Implied Correlation (IC) Index. The IC Index is a market estimate of the average correlation of the stocks that comprise the S&P500 Index, calculated by using options prices. You can read more about the IC Index here.
Here we have two daily charts: the S&P500 (top) and the CBOE S&P500 IC Index (bottom). We will graphically demonstrate that Volatility Based Technical Analysis (VBTA) can be used to attain actionable info, even out of small changes in correlation readings.
On the IC Index, I have circled the instances where VBTA from our MetaSwing for Bloomberg Professional has signaled significant moves in volatility (objective signals) in the IC Index. On the S&P500 chart accompanied with it, I have drawn arrows that correspond to said moves. Green arrows are a result of the tops in correlation while red arrows are the bottoms.
What is the conclusion? Bottoms in the IC Index are uh-hem, “correlated” to tops in the market and vice versa. This is actually counter-intuitive. Most analysts will warn when correlations are high, but it’s actually and actionably already too late. From our analysis regarding volatility of correlation and from knowing when “how high is too high” and “how low is too low,” it actually works in quite the reverse. Additionally, getting long something when the masses are not participating can mean that there is sidelined money to push your asset/security higher and vice versa.
This is an intriguing zero gravity idea (not being weighed down as to what cannot be accomplished): technical analysis, used quantitatively, and incorporating volatility can parse out actionable intel even from correlation. We have written previously about using VBTA to dissect stocks, bonds, fx, commodities, options charts, implieds, and volatility. Now we see that it can be used for correlation. I find this satisfying because it goes beyond, “correlations are high, so let’s stay alert” type of vague analysis that we often see in the media which is often useless, or worse counter-productive.
Taking this idea just one step further, you could marry the idea of a high probability correlation signal with other high probability concepts like volatility based support/resistance. Now you have signals of high or low correlation that are actionable with a level to lean against and manage risk and levels to calculate reward. This would also be an approach that not many are taking, with all the benefits that such an approach holds. But, most of all, this exercise has shown what is capable through creative application of objective technical analysis.
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