TY  - UNPD
A1  - Haas, Markus
A1  - Mittnik, Stefan
A1  - Paolella, Marc S.
T1  - Mixed normal conditional heteroskedasticity
T2  - Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 2002,10
N2  - 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
T3  - CFS working paper series - 2002, 10 
KW  - Finance
KW  - GARCH
KW  - Kurtosis
KW  - Skewness
KW  - Stationarity
KW  - USA
KW  - ARCH-Prozess
KW  - GARCH-Prozess
KW  - Volatilität
KW  - Stochastischer Prozess
KW  - Börsenkurs
KW  - Kapitalertrag
KW  - Kapitalgewinn
KW  - Schätzung
Y1  - 2002
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/4494
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30-10005
IS  - Version September 2002
ER  -