We are indebted to Gerald Gay (the editor) and the anonymous referee, whose comments and suggestions significantly improved the paper. We would also like to thank Yacine Ait-Sahalia, Gary Caton, Charles Cao, Kalok Chan, Fangjian Fu, Bruce Grundy, Louis Ederington, Lobos Pástor, Krishna Ramaswamy, Robert Stambaugh, Avanidhar Subrahmanyam, Marti Subrahmanyam, and Chu Zhang, as well as participants at the 2006 Singapore Management University summer camp and the 2006 China International Conference in Finance. Special thanks to Justin Chan for helping constructing the liquidity measures. We are grateful to the Office for Research of Singapore Management University for financial support. We are solely responsible for any remaining errors.
EXPECTED VOLATILITY, UNEXPECTED VOLATILITY, AND THE CROSS-SECTION OF STOCK RETURNS
Article first published online: 14 JUN 2010
© 2010 The Southern Finance Association and the Southwestern Finance Association
Journal of Financial Research
Volume 33, Issue 2, pages 103–123, Summer 2010
How to Cite
Chua, C. T., Goh, J. and Zhang, Z. (2010), EXPECTED VOLATILITY, UNEXPECTED VOLATILITY, AND THE CROSS-SECTION OF STOCK RETURNS. Journal of Financial Research, 33: 103–123. doi: 10.1111/j.1475-6803.2010.01264.x
- Issue published online: 14 JUN 2010
- Article first published online: 14 JUN 2010
The existing literature finds conflicting results on the cross-sectional relation between expected returns and idiosyncratic volatility. We contend that at the firm level, the sample correlation between unexpected returns and expected idiosyncratic volatility can cloud the true relation between the expected return and expected idiosyncratic volatility. We show strong evidence that unexpected idiosyncratic volatility is positively related to unexpected returns. Using unexpected idiosyncratic volatility to control for unexpected returns, we find expected idiosyncratic volatility to be significantly and positively related to expected returns. This result holds after controlling for various firm characteristics, and it is robust across different sample periods.