Modelling and predicting market risk with Laplace-Gaussian mixture distributions
Markus Haas (),
Stefan Mittnik and
Applied Financial Economics, 2006, vol. 16, issue 15, 1145-1162
While much of classical statistical analysis is based on Gaussian distributional assumptions, statistical modelling 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 modelling 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 modelling and, in fact, also nests the Gaussian and Laplace.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9) Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to subscribers.
Working Paper: Modeling and predicting market risk with Laplace-Gaussian mixture distributions (2005)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:taf:apfiec:v:16:y:2006:i:15:p:1145-1162
Ordering information: This journal article can be ordered from
Access Statistics for this article
Applied Financial Economics is currently edited by Anita Phillips
More articles in Applied Financial Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().