Volatility forecasting with range-based EGARCH models

Published

Journal Article

We provide a simple, yet highly effective framework for forecasting return volatility by combining exponential generalized autoregressive conditional heteroscedasticity models with data on the range. Using Standard and Poor's 500 index data for 1983-2004, we demonstrate the importance of a long-memory specification, based on either a two-factor structure or fractional integration, that allows for some asymmetry between market returns and volatility innovations. Out-of-sample forecasts reinforce the value of both this specification and the use of range data in the estimation. We find substantial forecastability of volatility as far as 1 year from the end of the estimation period, contradicting the return-based conclusions of West and Cho and of Christoffersen and Diebold that predicting volatility is possible only for short horizons. © 2006 American Statistical Association Journal of Business & Economic Statistics.

Full Text

Duke Authors

Cited Authors

  • Brandt, MW; Jones, CS

Published Date

  • October 1, 2006

Published In

Volume / Issue

  • 24 / 4

Start / End Page

  • 470 - 486

International Standard Serial Number (ISSN)

  • 0735-0015

Digital Object Identifier (DOI)

  • 10.1198/073500106000000206

Citation Source

  • Scopus