Modeling and predicting market risk with Laplace-Gaussian mixture distributions

Markus Haas, Stefan Mittnik and Marc S. Paolella

No 2005/11, CFS Working Paper Series from Center for Financial Studies (CFS)

Abstract: 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.

Keywords: GARCH; hyperbolic distribution; kurtosis; Laplace distribution; mixture distributions; stock market returns (search for similar items in EconPapers)
JEL-codes: C16 C50 (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Modelling and predicting market risk with Laplace-Gaussian mixture distributions (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfswop:200511

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