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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.
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https://hal-hec.archives-ouvertes.fr/hal-00478472
Contributor : Antoine Haldemann <>
Submitted on : Friday, April 30, 2010 - 2:53:09 PM
Last modification on : Monday, December 2, 2019 - 4:46:02 PM

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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⟩

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