A Time-varying Mixing Multiplicative Error Model for Realized Volatility

Giovanni De Luca () and Giampiero Gallo ()

Econometrics Working Papers Archive from Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti"

Abstract: In this paper we model the dynamics of realized volatility as a Multiplicative Error Model with a mixture of distributions for the innovation term with time-varying mixing weights forced by past behavior of volatility. The mixture considers innovations as a source of time-varying volatility of volatility and is able to capture the right tail behavior of the distribution of volatility. The empirical results show that there is no substantial difference in the one-step ahead conditional expectations obtained according to various mixing schemes but that fixity of mixing weights may be a binding constraint in deriving accurate quantiles of the predicted distribution.

Keywords: Multiplicative Error Models; Realized Volatility; Mixture Distributions (search for similar items in EconPapers)
JEL-codes: C22 C51 C53 (search for similar items in EconPapers)
Pages: 21
Date: 2010-04
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 (1)

Downloads: (external link)
https://labdisia.disia.unifi.it/ricerca/pubblicazi ... s/2010/wp2010_03.pdf (application/pdf)

Related works:
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:fir:econom:wp2010_03

Access Statistics for this paper

More papers in Econometrics Working Papers Archive from Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti" Viale G.B. Morgagni, 59 - I-50134 Firenze - Italy. Contact information at EDIRC.
Bibliographic data for series maintained by Fabrizio Cipollini ().