Regime-Switching and the Estimation of Multifractal Processes

43 Pages Posted: 20 Jul 2003 Last revised: 22 May 2022

See all articles by Laurent E. Calvet

Laurent E. Calvet

SKEMA Business School; CEPR

Adlai J. Fisher

University of British Columbia (UBC) - Sauder School of Business

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Date Written: July 2003

Abstract

We propose a discrete-time stochastic volatility model in which regime switching serves three purposes. First, changes in regimes capture low frequency variations, which is their traditional role. Second, they specify intermediate frequency dynamics that are usually assigned to smooth autoregressive processes. Finally, high frequency switches generate substantial outliers. Thus, a single mechanism captures three important features of the data that are typically addressed as distinct phenomena in the literature. Maximum likelihood estimation is developed and shown to perform well in finite sample. We estimate on exchange rate data a version of the process with four parameters and more than a thousand states. The estimated model compares favorably to earlier specifications both in- and out-of-sample. Multifractal forecasts slightly improve on GARCH(1,1) at daily and weekly intervals, and provide considerable gains in accuracy at horizons of 10 to 50 days.

Suggested Citation

Calvet, Laurent E. and Fisher, Adlai J., Regime-Switching and the Estimation of Multifractal Processes (July 2003). NBER Working Paper No. w9839, Available at SSRN: https://ssrn.com/abstract=423312

Laurent E. Calvet (Contact Author)

SKEMA Business School ( email )

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France

CEPR ( email )

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Adlai J. Fisher

University of British Columbia (UBC) - Sauder School of Business ( email )

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Canada
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604-822-4695 (Fax)

HOME PAGE: http://finance.sauder.ubc.ca/~fisher

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