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

Laurent Calvet

Journal of Financial Econometrics, 2004, vol. 2, issue 1, 49-83

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. Copyright 2004, Oxford University Press.

Date: 2004
References: Add references at CitEc
Citations: View citations in EconPapers (148)

Downloads: (external link)
http://hdl.handle.net/10.1093/jjfinec/nbh003 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: How to Forecast Long-Run Volatility: Regime Switching and the Estimation of Multifractal Processes (2004)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:jfinec:v:2:y:2004:i:1:p:49-83

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Journal of Financial Econometrics is currently edited by Allan Timmermann and Fabio Trojani

More articles in Journal of Financial Econometrics from Oxford University Press Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK. Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().