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 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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Author:Markus Haas, Stefan Mittnik, Marc S. Paolella
Parent Title (German):Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 2005,11
Series (Serial Number):CFS working paper series (2005, 11)
Document Type:Working Paper
Year of Completion:2005
Year of first Publication:2005
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2005/06/13
Tag:GARCH; Hyperbolic Distribution; Kurtosis; Laplace Distribution; Mixture Distributions; Stock Market Returns
GND Keyword:Marktrisiko; Laplace-Verteilung; Gauß-Funktion
Issue:March 2005
Institutes:Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS)
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
Licence (German):License LogoDeutsches Urheberrecht