Volatility forecasting with range-based EGARCH models
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.
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- Econometrics
- 49 Mathematical sciences
- 38 Economics
- 35 Commerce, management, tourism and services
- 15 Commerce, Management, Tourism and Services
- 14 Economics
- 01 Mathematical Sciences
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Econometrics
- 49 Mathematical sciences
- 38 Economics
- 35 Commerce, management, tourism and services
- 15 Commerce, Management, Tourism and Services
- 14 Economics
- 01 Mathematical Sciences