Long memory and nonlinearities in realized volatility: a Markov switching approach

S. Bordignon and Davide Raggi

Working Papers from Dipartimento Scienze Economiche, Universita' di Bologna

Abstract: Goal of this paper is to analyze and forecast realized volatility through nonlinear and highly persistent dynamics. In particular, we propose a model that simultaneously captures long memory and nonlinearities in which level and persistence shift through a Markov switching dynamics. We consider an efficient Markov chain Monte Carlo (MCMC) algorithm to estimate parameters, latent process and predictive densities. The insample results show that both long memory and nonlinearities are significant and improve the description of the data. The out-sample results at several forecast horizons, show that introducing these nonlinearities produces superior forecasts over those obtained from nested models.

Date: 2010-02
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://amsacta.unibo.it/4547/1/694.pdf (application/pdf)

Related works:
Journal Article: Long memory and nonlinearities in realized volatility: A Markov switching approach (2012)
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:bol:bodewp:694

Access Statistics for this paper

More papers in Working Papers from Dipartimento Scienze Economiche, Universita' di Bologna Contact information at EDIRC.
Bibliographic data for series maintained by Dipartimento Scienze Economiche, Universita' di Bologna ().