Modeling and predicting market risk with Laplace-Gaussian mixture distributions

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 
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. Klassifikation: C16, C50 . March 2005.
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Metadaten
Author:Markus Haas, Stefan Mittnik, Marc S. Paolella
URN:urn:nbn:de:hebis:30-10872
Series (Serial Number):CFS working paper series (2005, 11)
Document Type:Working Paper
Language:English
Date of Publication (online):2005/06/13
Year of first Publication:2005
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2005/06/13
Tag:GARCH ; Hyperbolic Distribution ; Kurtosis ; Laplace Distribution ; Mixture Distributions ; Stock Market Returns
Source:CFS working paper ; 2005,11
HeBIS PPN:197305989
Institutes:Center for Financial Studies (CFS)
Dewey Decimal Classification:330 Wirtschaft
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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