Resource title

Modeling and predicting market risk with Laplace-Gaussian mixture distributions

Resource image

image for OpenScout resource :: Modeling and predicting market risk with Laplace-Gaussian mixture distributions

Resource description

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.

Resource author

Markus Haas, Stefan Mittnik, Marc S. Paolella

Resource publisher

Resource publish date

Resource language

eng

Resource content type

text/html

Resource resource URL

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

Resource license

Adapt according to the presented license agreement and reference the original author.