Relative Value single-stock Volatility

Variance swaps can also be used to trade spreads between single-stock volatilities.

-  As a way of partially hedging the variance of a single-name against wider market/sector moves

- To directly trade relative value single-stock volatility.


Two possible methods for assessing relative value single stock variances: 

  • temporal regression
  • cross-sectional regression


Temporal Regression

It works by looking at the evolution of the ratio (or spread) of the implied variances between two stocks.

A z-score can be computed to express how far the ratio is from its LT average.

Assuming mean-reverting tendencies --> pairs with large z-scores --> potential opportunities for volatility pairs trades. 

This applies particularly to well correlated stocks, typically from the same sector with similar volatility characteristics.


Of course sometimes a divergence in implied variances of two stocks whose variances were previously highly correlated may simply reflect real changes in the underlying market. To some extent we can screen out these type of scenarios by also considering the z-score arising from the ratios of realised volatilities. If the realised volatility ratio has recently diverged from its LT average in the same way as the ratio of implied variances, then the change in the implied variance ratio may be justified, and no trading opportunity exists.


--> Look for pairs with high z-score in their IV ratio, which is not reflected in a corresponding high z-score for their RV ratio. 


Back-test of this method gives encouraging results. 


Cross-sectional regression

An alternative means of screening for rich/cheap single-stock variance is to use a cross-sectional regression methodology.

This regression is trying to model all current stock IVs as a multi-linear function of a number of various other properties:

  • Historic volatility (should usually be the main factor determining a stock’s IV)

  • Stock beta vs. the market (high beta should translate into high volatility)

  • Dividend yield (high yielding should perhaps be less volatile)

  • 3-month return (poor performers may tend to have higher volatility)

  • CDS spread (reflects a company’s credit risk and leverage, and should correlate with IV) 


Backtest of this method gives good results. 

This method can be used to select stocks with cheap implied variance to trade against index variance in a dispersion trade. 


In any case, investors should consider if they wish to be exposed to the relative realised variance or implied variance of the stocks. 


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