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Mixed normal conditional heteroskedasticity

Markus Haas (markus.haas@qber.uni-kiel.de), Stefan Mittnik and Marc S. Paolella

No 2002/10, CFS Working Paper Series from Center for Financial Studies (CFS)

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.

Keywords: Finance; GARCH; Kurtosis; Skewness; Stationarity (search for similar items in EconPapers)
JEL-codes: C22 C51 G10 (search for similar items in EconPapers)
Date: 2002
References: Add references at CitEc
Citations: View citations in EconPapers (30)

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Journal Article: Mixed Normal Conditional Heteroskedasticity (2004) Downloads
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