Arbeitspapier

Modeling and predicting market risk with Laplace-Gaussian mixture distributions

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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.

Language: Englisch

Bibliographic citation: Series: CFS Working Paper ; No. 2005/11

Classification: Wirtschaft
Econometric Modeling: General

Subject: GARCH
hyperbolic distribution
kurtosis
Laplace distribution
mixture distributions
stock market returns

Event: Geistige Schöpfung

(who): Haas, Markus
Mittnik, Stefan
Paolella, Marc S.

Event: Veröffentlichung

(who): Goethe University Frankfurt, Center for Financial Studies (CFS)

(where): Frankfurt a. M.

(when): 2005

Handle: http://hdl.handle.net/10419/25453

URN: urn:nbn:de:hebis:30-10872

Last update: 10.03.2025, 11:42 AM CET

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Object type

Arbeitspapier

Associated

Haas, Markus
Mittnik, Stefan
Paolella, Marc S.
Goethe University Frankfurt, Center for Financial Studies (CFS)

Time of origin

2005

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