The bias in time series volatility forecasts.

SelectedWorks Author Profiles:

Wei Guan

Document Type


Publication Date


Date Issued

January 2010

Date Available

April 2013




By Jensen's inequality, a model's forecasts of the variance and standard deviation of returns cannot both be unbiased. This study explores the bias in GARCH type model forecasts of the standard deviation of returns, which we argue is the more appropriate volatility measure for most financial applications. For a wide variety of markets, the GARCH, EGARCH, and GJR (or TGARCH) models tend to persistently over-estimate the standard deviation of returns, whereas the ARLS model of L. Ederington and W. Guan (2005a) does not. Furthermore, the GARCH and GJR forecasts are especially biased following high volatility days, which cause a large jump in forecast volatility, which is rarely fully realized.


Abstract only. Full-text article is available through licensed access provided by the publisher. Published in Journal of Futures Markets, 30(4), 305-323. DOI: 10.1002/fut.20417. Members of the USF System may access the full-text of the article through the authenticated link provided.




John Wiley & Sons

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.