Buy rough, sell smooth

Abstract

Recent work has documented roughness in the time series of stock market volatility and investigated its implications for option pricing. We study a strategy for trading stocks based on measures of their implied and realized roughness. A strategy that goes long the roughest-volatility stocks and short the smoothest-volatility stocks earns statistically significant excess annual returns of 6% or more, depending on the time period and strategy details. The profitability of the strategy is not explained by standard factors. We compare alternative measures of roughness in volatility and find that the profitability of the strategy is greater when we sort stocks based on implied rather than realized roughness. We interpret the profitability of the strategy as compensation for near-term idiosyncratic event risk.

Keywords:

• Volatility
• Fractional Brownian motion
• Trading strategies
• Volatility skew

JEL Codes:

• G12–Asset Pricing
• G1–General Financial Markets
• G–FinancialEconomics
• G13–Contingent Pricing$|$Futures Pricing
• G1–General financial markets
• G–financial Economics

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

† We use the non-flat Parzen kernel as implemented in Kevin Sheppard's toolbox at https://www.kevinsheppard.com/MFE_Toolbox.

‡ In tests of alternative estimation methods on simulated data, for which we know H, we have found that the main source of error is the estimation of the daily integrated variances ${\hat{\sigma }}_d^2$ from intraday returns, rather than the estimation of H from the daily volatilities.

† http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

† According to Mathieu Rosenbaum (personal communication), Jusselin and Rosenbaum (2018) implies a longer transient price impact when H is smaller, which would be consistent with the correlations we find.