Arbeitspapier
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: CFS Working Paper ; No. 2005/11
- Klassifikation
-
Wirtschaft
Econometric Modeling: General
- Thema
-
GARCH
hyperbolic distribution
kurtosis
Laplace distribution
mixture distributions
stock market returns
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Haas, Markus
Mittnik, Stefan
Paolella, Marc S.
- Ereignis
-
Veröffentlichung
- (wer)
-
Goethe University Frankfurt, Center for Financial Studies (CFS)
- (wo)
-
Frankfurt a. M.
- (wann)
-
2005
- Handle
- URN
-
urn:nbn:de:hebis:30-10872
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Haas, Markus
- Mittnik, Stefan
- Paolella, Marc S.
- Goethe University Frankfurt, Center for Financial Studies (CFS)
Entstanden
- 2005
Ähnliche Objekte (12)
Modeling and predicting market risk with Laplace-Gaussian mixture distributions
Matrix variate generalized laplace distributions
Discrete mixture representations of spherical distributions
Gaussian Mixture Separation and Denoising on Parameterized Varieties
Invariant image object recognition using Gaussian mixture densities
Trust-Region Variational Inference with Gaussian Mixture Models
Invariant image object recognition using Gaussian mixture densities
Prediction of volatility with penalized mixture distributions
Interpolation of Gaussian mixture models, and other directions in OMT
Flexible Mixture-Amount Models for Business and Industry using Gaussian Processes
Indexing and knowledge discovery of gaussian mixture models and multiple-instance learning
Estimating and Predicting Household Expenditures and Income Distributions
Modeling and predicting market risk with Laplace-Gaussian mixture distributions
Matrix variate generalized laplace distributions
Discrete mixture representations of spherical distributions
Gaussian Mixture Separation and Denoising on Parameterized Varieties
Invariant image object recognition using Gaussian mixture densities
Trust-Region Variational Inference with Gaussian Mixture Models
Invariant image object recognition using Gaussian mixture densities
Prediction of volatility with penalized mixture distributions
Interpolation of Gaussian mixture models, and other directions in OMT
Flexible Mixture-Amount Models for Business and Industry using Gaussian Processes
Indexing and knowledge discovery of gaussian mixture models and multiple-instance learning
Estimating and Predicting Household Expenditures and Income Distributions
Modeling and predicting market risk with Laplace-Gaussian mixture distributions
Matrix variate generalized laplace distributions
Discrete mixture representations of spherical distributions
Gaussian Mixture Separation and Denoising on Parameterized Varieties
Invariant image object recognition using Gaussian mixture densities
Trust-Region Variational Inference with Gaussian Mixture Models
Invariant image object recognition using Gaussian mixture densities
Prediction of volatility with penalized mixture distributions
Interpolation of Gaussian mixture models, and other directions in OMT
Flexible Mixture-Amount Models for Business and Industry using Gaussian Processes
Indexing and knowledge discovery of gaussian mixture models and multiple-instance learning