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

Laurent Calvet and Adlai Fisher

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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.

Keywords: forecasting; long memory; Markov-switching multifractal (MSM); closed-form likelihood; scaling; stochastic volatility; volatility component; Vuong test (search for similar items in EconPapers)
Date: 2004
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Citations: View citations in EconPapers (132)

Published in Journal of Financial Econometrics, 2004, Vol.2,n°1, pp.49-83. ⟨10.1093/jjfinec/nbh003⟩

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Journal Article: How to Forecast Long-Run Volatility: Regime Switching and the Estimation of Multifractal Processes (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-00478472

DOI: 10.1093/jjfinec/nbh003

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