Generalized Additive Models for Conditional Dependence Structures

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Version: Final published version
ID Serval
serval:BIB_260D2941D595
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Generalized Additive Models for Conditional Dependence Structures
Périodique
Journal of Multivariate Analysis
Auteur(s)
Vatter T., Chavez-Demoulin V.
ISSN
0047-259X
Statut éditorial
Publié
Date de publication
10/2015
Peer-reviewed
Oui
Volume
141
Pages
147-167
Langue
anglais
Résumé
We develop a generalized additive modeling framework for taking into account the effect of predictors on the dependence structure between two variables. We consider dependence or concordance measures that are solely functions of the copula, because they contain no marginal information: rank correlation coefficients or tail-dependence coefficients represent natural choices. We propose a maximum penalized log-likelihood estimator, derive its n-consistency and asymptotic normality, discuss details of the estimation procedure and the selection of the smoothing parameter. Finally, we present the results from a simulation study and apply the new methodology to a real dataset. Using intraday asset returns, we show that an intraday dependence pattern, due to the cyclical nature of market activity, is shaped similarly to the individual conditional second moments.
Mots-clé
Conditional rank correlations, Copula, Penalized log-likelihood, Regression splines, Semiparametric modeling, Intraday financial returns
Web of science
Création de la notice
13/07/2015 21:40
Dernière modification de la notice
20/08/2019 14:04
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