On the Conditional Performance of the Ivol Anomaly

This paper explores the issue that the idiosyncratic volatility (IVOL) puzzle documented in Ang et al. (2006) can partly be attributed to estimation bias when assessing systematic risk with information not in investor's information set. We first analytically show that there exists a conditioning bias in IVOL estimates when and IVOL are estimated using rolling regressions with contemporaneous daily return data. To mitigate this estimation bias, we suggest using conditional factor model as in Avramov and Chordia (2006) to estimate the systematic risk and IVOL. Empirically, we find that using Q-theory based firm characteristics as the conditioning variables, such as size, book-to-market and operation leverage, the conditionally estimated IVOL does not command a negative return premium and the associated benchmark-adjusted abnormal return is also much smaller than that in the original IVOL puzzle. The empirical findings are robust in both the U.S. and Chinese stock markets