Entropy Densities with an Application to Autoregressive Conditional Skewness and Kurtosis

Détails

ID Serval
serval:BIB_0CCC00C94BA1
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Entropy Densities with an Application to Autoregressive Conditional Skewness and Kurtosis
Périodique
Journal of Econometrics
Auteur(s)
Jondeau E., Rockinger M.
ISSN
0304-4076
Statut éditorial
Publié
Date de publication
01/2002
Peer-reviewed
Oui
Volume
106
Numéro
1
Pages
119-142
Langue
anglais
Résumé
The entropy principle yields, for a given set of moments, a density that involves the smallest amount of prior information. We first show how entropy densities may be constructed in a numerically efficient way as the minimization of a potential. Next, for the case where the first four moments are given, we characterize the skewness?kurtosis domain for which densities are defined. This domain is found to be much larger than for Hermite or Edgeworth expansions. Last, we show how this technique can be used to estimate a GARCH model where skewness and kurtosis are time varying. We find that there is little predictability of skewness and kurtosis for weekly data.
Mots-clé
Semi-nonparametric estimation, Time-varying skewness and kurtosis, GARCH
Création de la notice
19/11/2007 10:28
Dernière modification de la notice
03/03/2018 13:38
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