HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

How to Forecast Long-Run Volatility: Regime Switching and the Estimation of Multifractal Processes

Abstract : We propose a discrete-time stochastic volatility model in which regime switching serves three purposes. First, changes in regimes capture low-frequency variations. Second, they specify intermediate-frequency dynamics usually assigned to smooth autoregressive transitions. Finally, high-frequency switches generate substantial outliers. Thus a single mechanism captures three features that are typically viewed as distinct in the literature. Maximum-likelihood estimation is developed and performs well in finite samples. Using exchange rates, we estimate a version of the process with four parameters and more than a thousand states. The multifractal outperforms GARCH, MS-GARCH, and FIGARCH in- and out-of-sample. Considerable gains in forecasting accuracy are obtained at horizons of 10 to 50 days.
Document type :
Journal articles
Complete list of metadata

https://hal-hec.archives-ouvertes.fr/hal-00478472
Contributor : Antoine Haldemann Connect in order to contact the contributor
Submitted on : Friday, April 30, 2010 - 2:53:09 PM
Last modification on : Friday, December 18, 2020 - 5:30:02 PM

Identifiers

Collections

Citation

Laurent-Emmanuel Calvet, Adlai J. Fisher. How to Forecast Long-Run Volatility: Regime Switching and the Estimation of Multifractal Processes. Journal of Financial Econometrics, Oxford University Press (OUP), 2004, Vol.2,n°1, pp.49-83. ⟨10.1093/jjfinec/nbh003⟩. ⟨hal-00478472⟩

Share

Metrics

Record views

116