A recent Bloomberg news story by Nikolaj Gammeltoft, Nick Taborek and Aubrey Pringle noted that –
“U.S. options traders are convinced that profits, buybacks and takeovers will exert a greater influence on stock prices in coming months, sending an index tracking expectations for lockstep moves to a six-year low. The Chicago Board Options Exchange S&P 500 Implied Correlation Index tumbled … The gauge, which uses options to indicate how closely stocks in the Standard & Poor’s 500 Index will move together, … The decline in the index shows investors are less concerned that economic and political turmoil will whipsaw stock prices. ..”
The CBOE S&P 500® Implied Correlation Index (JCJ-E, Jan. 2014) fell from 87.1 on Dec. 14, 2011, to its all-time low of 36.07 on Oct. 18, 2013, a drop of 59%. On Oct. 28, 2013 the JCJ-E Index closed at 37.59.
The CBOE S&P 500 Implied Correlation Index measures expected average correlation of the S&P 500 using SPX option implied volatilities and a weighted portfolio of the implied volatilities of options on stocks in an SPX “tracking basket,” a subset of the S&P 500 comprised of the 50 largest components as measured by market capitalization. The CBOE S&P 500 Implied Correlation Indexes measure changes in the relative premium between index options and single-stock options.
The CBOE S&P 500 Implied Correlation Indexes may be used to provide trading signals for a strategy known as volatility dispersion (or correlation) trading. For example, a long volatility dispersion trade is characterized by selling at-the-money index option straddles and purchasing at-the-money straddles in options on index components.
Another possible way to use the Implied Correlation Index for a trading strategy is as follows — when implied correlation is low, single-stock option premiums can be richly priced relative to index options. Therefore, it may be profitable to sell the richly priced single-stock options and buy the relatively inexpensive index options.
Please visit www.cboe.com/JCJ for much more information and data history.