By: Steve Smith
Over the past few weeks, we’ve seen an incredible performance separation between growth and value with tech-laden Nasdaq 100 (QQQ) having declined over 10% in the past month while the Vanguard Value ETF (VTV) climbed 10% and is hitting new 52-week highs today.
Correlation refers to assets, sometimes of very different classes, moving up and down at the same time. In a paired trade we used the reduction of capital required and the options leverage to improve the risk/reward profile.
Dispersion refers to how much more or less the individual components within a broader group, such as an index, will move relative to the whole. Here, the application of options is more tightly embedded in the strategy itself; namely leveraging the differential of volatilities between the individual stocks and the total index they comprise
In a world of lower correlation, there should also be higher dispersion. Both benefit investors with superior stock-picking skills.
Sum of the Parts
By definition, an index is a portfolio of many singular components that in theory should be less risky than any one individual stock. A portfolio dilutes company-specific risk and is only subject to market risk, while an individual stock is exposed to both risks, which are typically priced into the average stock option. This is why dispersion strategies typically look to short the index options and buy options on the individual components.
A volatility dispersion trading is a popular hedged strategy designed to take advantage of relative value differences in volatilities between an index and a basket of the component stocks. This would allow one to leverage large price moves without incurring exposure to changes in implied volatility, or vega risk. Vega measures the expected change in the dollar value of an option for each unit change in implied volatility.
A strict application of a volatility dispersion strategy is not practical for an individual investor. It requires high levels of computing power to crunch data such as implied vs. historical measures of both the indices and the individual components, implied correlation, equivalency weightings, stock-specific variances, contributions to the index to find small statistical advantages that they can use to execute in large numbers at low costs. It’s also labor-intensive, requiring ongoing adjustments to keep everything in balance.
Large hedge funds might be able to do this but going out to row 1,282, column ZZZ, on a spreadsheet to lock in a nickel doesn’t sound like much fun to me. However, we can still apply the underlying concept to create a stripped-down version of a dispersion strategy. We’ll use common sense instead of a supercomputer.
For retail trades, exchange-traded funds (ETFs) offer a good vehicle for applying a dispersion strategy. Thanks to their focus on various sectors and liquidity sector one can sell options on the lower volatility index and buy volatility on a handful of individual names.