Arbeitspapier

Mixed normal conditional heteroskedasticity

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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.

Language: Englisch

Bibliographic citation: Series: CFS Working Paper ; No. 2002/10

Classification: Wirtschaft
Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
Model Construction and Estimation
General Financial Markets: General (includes Measurement and Data)

Subject: Finance
GARCH
Kurtosis
Skewness
Stationarity

Event: Geistige Schöpfung

(who): Haas, Markus
Mittnik, Stefan
Paolella, Marc S.

Event: Veröffentlichung

(who): Goethe University Frankfurt, Center for Financial Studies (CFS)

(where): Frankfurt a. M.

(when): 2002

Handle: http://hdl.handle.net/10419/78068

URN: urn:nbn:de:hebis:30-10005

Last update: 10.03.2025, 11:45 AM CET

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Object type

Arbeitspapier

Associated

Haas, Markus
Mittnik, Stefan
Paolella, Marc S.
Goethe University Frankfurt, Center for Financial Studies (CFS)

Time of origin

2002

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