How asymmetric is U.S. stock market volatility?

SelectedWorks Author Profiles:

Wei Guan

Document Type


Publication Date


Date Issued

January 2010

Date Available

April 2013




This paper explores differences in the impact of equally large positive and negative surprise return shocks in the aggregate U.S. stock market on: (1) the volatility predictions of asymmetric time-series models, (2) implied volatility, and (3) realized volatility. Following large negative surprise return shocks, both asymmetric time-series models (such as the EGARCH and GJR models) and implied volatility predict an increase in volatility and, consistent with this, ex post realized volatility normally rises as predicted. Following large positive return shocks, asymmetric time-series models predict an increase in volatility (albeit a much smaller increase than following a negative shock of the same magnitude), but both implied and realized volatilities generally fall sharply. While asymmetric time-series models predict a decline in volatility following near-zero returns, both implied and realized volatility are normally little changed from levels observed prior to the stable market. The reasons for the differences are explored.


Abstract only. Full-text article is available through licensed access provided by the publisher. Published in Journal of Financial Markets, 13(2), 225-248. DOI: 10.1016/j.finmar.2009.10.001. Members of the USF System may access the full-text of the article through the authenticated link provided.





Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.