CBOE Launches Implied Correlation Index

Volatility watchers and risk managers have a new index to watch, starting next Monday: the CBOE S&P 500 Implied Correlation Index. The CBOE announced the new index in a press release yesterday, noting that the Implied Correlation Index will be tied to two different option maturities - January 2010 (ICJ) and January 2011 (JCJ). The values of ICJ and JCJ will be disseminated by the CBOE every 15 seconds during the trading day, as is done with the VIX.

The Implied Correlation Index does not calculate the correlations among the entire S&P 500 index. Instead, it utilizes a “tracking basket” of the 50 largest components of the SPX as measured by market capitalization. For more on the calculations involved in the Implied Correlation Index and related materials, check out the Implied Correlation Index splash page, the Implied Correlation Index white paper and historical data going back to the beginning of 2007.

I will discuss this index on the blog going forward, as it is clearly an important piece of the VIX valuation mystery. For now, readers may be interested to see how SPX component correlations (blue line) have compared with the VIX (red line) during the recent bear market.

[source: CBOE]

Index Volatility and Component Correlation

While I was sleeping soundly this morning, Adam at Daily Options Report was already up and deconstructing the volatility of the S&P 500 index. Drawing upon some data compiled by Bespoke Investment Group that show increasing correlation across the S&P 500 sectors during the course of the past six months, Adam concludes that the current high levels of index volatility (VIX, VXN, RVX, VXO, VXD) are due in part to this recent increase in correlation among the components of the S&P 500.

In Correlation Station (no relation to Terrapin Station), Adam breaks it down as follows:

“You can boil index volatility down to two basic factors. One is the volatility of the component stocks, the other is the correlation between those very stocks. And they can very much offset each other. Imagine a world where half the stocks were moving violently and basically trending in one direction, and the other half was moving violently the other way. Index volatility would be very low as the moves would pretty much offset each other.

What we have now is the opposite. Stocks aren't that cosmically volatile, but they are all moving in relative unison. Ergo index volatility is theoretically *high* relative to individual stock volatility.”

There is not much I can do to improve upon that explanation.

While an understanding of the correlation phenomenon is important, I’m sure many are wondering if the current situation is tradeable. For what little it is worth, I am not going to be taking trades that should be winners if correlations start unwinding, but I encourage those who are interested in this subject (yet another type of mean reversion play) to check out Adam’s thinking about some possible trades along these lines.

Correlation Ideation

Let’s say, for the sake of argument, that you are intrigued by the 71% gains that MOS has logged in the past eight weeks in Portfolio A1, but for whatever reason do not want to own that particular stock. Perhaps you have an opinion that the fertilizer stocks are overbought or that a supercycle is just beginning in this sector. Which stocks should you be looking at? I recommend visits to three free web sites that can help you answer this and other related questions: Market Topology; Sector SPDR Correlation Tracker; and DeepMarket.com’s correlation tool. Each of these sites has some particular strengths that I discuss below.

My first stop to evaluate correlation data is usually at MarketTopology.com. Once there, you need to click on the Equities Markets: USA link to arrive at their “i-work” page. From here, just enter the ticker and either click on the ‘Calculate’ button to return data in a table (usually the better choice) or try ‘Map’ to see a graphical representation of the securities with the highest correlation. There are several other boxes you can use to filter the results; these should be self-explanatory and ripe for experimentation. In the case of MOS, the four highest correlations returned are POT, CF, AGU, and TRA – all companies in the fertilizer sector. The next two most correlated securities are both materials ETFs: VAW, the Vanguard Materials ETF; and IYM, the iShares Dow Jones Basic Materials Sector Index Fund. It is these types of discoveries that make tangential company and sector research more fun and interesting. Note also that the table also has a column for ‘Average Daily Volatility’ for those interested in identifying highly correlated stocks or ETF that are significantly more or less volatile than the baseline security.

Among the three sites discussed here, the ease of use award would probably go to the Sector SPDR Correlation Tracker, which simply asks for a ticker and generates three lists: highest correlation sector SPDRs; highest correlation stocks/ETFs; and lowest correlation stocks/ETFs. As an added bonus, you can generate java comparison charts for any four securities on these lists for additional analysis. Let’s say you are interested in the FXI, but prefer to take a position in an individual stock instead of the ETF. Using the Sector SPDR correlation tracker, you would be pointed in the direction of CHL, CEO, LFC, and BIDU.

At the bottom of the list is DeepMarket.com, which scores high for content, but low for aesthetics. Their correlation tracking tool lists the top 5 highest positive correlations and (lowest) negative correlations for the past 10, 30, 100, and 200 day trading periods. The site provides the correlation coefficient and a rudimentary line chart, but little else. What I do like is the ability to slice and dice correlations over four different time periods (the longer time periods probably provide the most value,) but apart from that feature, the other two sites are to be preferred.

I should mention that while I have focused on positive correlations here, each site provides a list of the most extreme negative correlations as well. While these generally are not as strong correlations as the positive correlations, they do provide and excellent jumping off point for someone looking to add securities to a portfolio that may be inversely correlated to some of the portfolio’s riskier holdings. This type of approach is admittedly more art than science at the individual security level, but for those unable to evaluate portfolio level correlation data, it is a substitute worth exploring.