Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/25453 
Erscheinungsjahr: 
2005
Schriftenreihe/Nr.: 
CFS Working Paper No. 2005/11
Verlag: 
Goethe University Frankfurt, Center for Financial Studies (CFS), Frankfurt a. M.
Zusammenfassung: 
While much of classical statistical analysis is based on Gaussian distributional assumptions, statistical modeling with the Laplace distribution has gained importance in many applied fields. This phenomenon is rooted in the fact that, like the Gaussian, the Laplace distribution has many attractive properties. This paper investigates two methods of combining them and their use in modeling and predicting financial risk. Based on 25 daily stock return series, the empirical results indicate that the new models offer a plausible description of the data. They are also shown to be competitive with, or superior to, use of the hyperbolic distribution, which has gained some popularity in asset-return modeling and, in fact, also nests the Gaussian and Laplace.
Schlagwörter: 
GARCH
hyperbolic distribution
kurtosis
Laplace distribution
mixture distributions
stock market returns
JEL: 
C16
C50
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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