Mixed normal conditional heteroskedasticity

Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH
Both unconditional mixed-normal distributions and GARCH models with fat-tailed conditional distributions have been employed for modeling financial return data. We consider a mixed-normal distribution coupled with a GARCH-type structure which allows for conditional variance in each of the components as well as dynamic feedback between the components. Special cases and relationships with previously proposed specifications are discussed and stationarity conditions are derived. An empirical application to NASDAQ-index data indicates the appropriateness of the model class and illustrates that the approach can generate a plausible disaggregation of the conditional variance process, in which the components' volatility dynamics have a clearly distinct behavior that is, for example, compatible with the well-known leverage effect. Klassifikation: C22, C51, G10
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Metadaten
Author:Markus Haas, Stefan Mittnik, Marc S. Paolella
URN:urn:nbn:de:hebis:30-10005
Parent Title (German):Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 2002,10
Series (Serial Number):CFS working paper series (2002, 10)
Document Type:Working Paper
Language:English
Year of Completion:2002
Year of first Publication:2002
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2005/06/13
Tag:Finance ; GARCH ; Kurtosis ; Skewness ; Stationarity
SWD-Keyword:ARCH-Prozess; Börsenkurs ; GARCH-Prozess; Kapitalertrag ; Kapitalgewinn ; Schätzung; Stochastischer Prozess; USA; Volatilität
Issue:Version September 2002
Pagenumber:35
HeBIS PPN:202108821
Institutes:Center for Financial Studies (CFS)
Dewey Decimal Classification:330 Wirtschaft
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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