Moment Component Analysis: An Illustration with International Stock Markets

Détails

ID Serval
serval:BIB_162DD5AAA610
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Moment Component Analysis: An Illustration with International Stock Markets
Périodique
Journal of Business and Economic Statistics
Auteur(s)
Jondeau E., Jurczenko  E., Rockinger M.
ISSN
0735-0015
Statut éditorial
Publié
Date de publication
2016
Peer-reviewed
Oui
Pages
1-23
Langue
anglais
Résumé
We describe a statistical technique, which we call Moment Component Analysis (MCA), that extends Principal Component Analysis (PCA) to higher co-moments such as co-skewness and co-kurtosis. This method allows us to identify the factors that drive co-skewness and co-kurtosis structures across a large set of series. We illustrate MCA using 44 international stock markets sampled at weekly frequency from 1994 to 2014. We find that both the co-skewness and the co-kurtosis structures can be summarized with a small number of factors. Using a rolling window approach, we show that these co-moments convey useful information about market returns, for systemic risk measurement and portfolio allocation, complementary to the information extracted from a standard PCA or from an Independent Component Analysis.
Mots-clé
PCA, Skewness, Kurtosis, Tensor, Random Matrix Theory, Portfolio analysis
Création de la notice
05/05/2017 11:39
Dernière modification de la notice
20/08/2019 13:45
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