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Longer-Term Time-Series Volatility Forecasts

Published online by Cambridge University Press:  02 July 2010

Louis H. Ederington  and
Wei Guan
Louis H. Ederington
Affiliation:
Price College of Business, University of Oklahoma, 205A Adams Hall, Norman, OK 73019. lederington@ou.edu
Wei Guan
Affiliation:
College of Business, University of South Florida St. Petersburg, 140 7th Ave. S., St. Petersburg, FL 33701. wguan@mail.usf.edu
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Abstract

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.

Type
Research Articles
Information
Journal of Financial and Quantitative Analysis , Volume 45 , Issue 4 , August 2010 , pp. 1055 - 1076
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2010

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