Monday, March 13, 2017 | 4:00pm-5:30pm | Room 337, HSBC Business School Building

Abstract

Commonality in idiosyncratic volatility cannot be completely explained by time-varying volatility. After removing the effects of time-varying volatility, idiosyncratic volatility innovations are still positively correlated. This result suggests correlated volatility shocks contribute to the comovement in idiosyncratic volatility. Motivated by this fact, we propose the Dynamic Factor Correlation (DFC) model, which fits the data well and captures the crosssectional correlations in idiosyncratic volatility innovations. We decompose the common factor in idiosyncratic volatility (CIV) of Herskovic et al. (2016) into the volatility innovation factor (VIN) and time-varying volatility factor (TVV), and find VIN and TVV capture similar expected return variation and both contribute towards the asset pricing power of CIV. A strategy that takes a long position in the portfolio with the lowest VIN and TVV betas, and a short position in the portfolio with the highest VIN and TVV betas earns average returns of 8.0% per year.