Longer-term time-series volatility forecasts.

SelectedWorks Author Profiles:

Wei Guan

Document Type


Publication Date


Date Issued

January 2010

Date Available

April 2013




Option pricing models and longer-term value-at-risk (VaR) models generally require volatility forecasts over horizons considerably longer than the data frequency. The typical recursive procedure for generating longer-term forecasts keeps the relative weights of recent and older observations the same for all forecast horizons. In contrast, we find that older observations are relatively more important in forecasting at longer horizons. We find that the Ederington and Guan (2005) model and a modified EGARCH (exponential generalized autoregressive conditional heteroskedastic) model in which parameter values vary with the forecast horizon forecast better out-of-sample than the GARCH (generalized autoregressive conditional heteroskedastic), EGARCH, and Glosten, Jagannathan, and Runkle (GJR) models across a wide variety of markets and forecast horizons.


Abstract only. Full-text article is available through licensed access provided by the publisher. Published in Journal of Financial and Quantitative Analysis, 45(4), 1055-1076. DOI: 10.1017/S0022109010000372. Members of the USF System may access the full-text of the article through the authenticated link provided.




Cambridge University Press

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.