Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/78068
Authors: 
Haas, Markus
Mittnik, Stefan
Paolella, Marc S.
Year of Publication: 
2002
Series/Report no.: 
CFS Working Paper 2002/10
Abstract: 
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.
Subjects: 
Finance
GARCH
Kurtosis
Skewness
Stationarity
JEL: 
C22
C51
G10
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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