Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/36655

TitleExtremes of scale mixtures of multivariate time series
Author(s)Ferreira, Helena
Ferreira, Marta Susana
KeywordsMultivariate extreme value theory
Tail dependence
Factor models
Issue date2015
PublisherElsevier
JournalJournal of Multivariate Analysis
CitationFerreira, H., & Ferreira, M. (2015). Extremes of scale mixtures of multivariate time series. Journal of Multivariate Analysis, 137, 82-99. doi: 10.1016/j.jmva.2015.02.002
Abstract(s)Factor models have large potencial in the modeling of several natural and human phenomena. In this paper we consider a multivariate time series $\mb{Y}_n$, n≥1, rescaled through random factors $\mb{T}_n$, n≥1, extending some scale mixture models in the literature. We analyze its extremal behavior by deriving the maximum domain of attraction and the multivariate extremal index, which leads to new ways to construct multivariate extreme value distributions. The computation of the multivariate extremal index and the characterization of the tail dependence show the interesting property of these models that however much it is the dependence within and between factors $\mb{T}_n$, n≥1, the extremal index of the model is unit whenever $\mb{Y}_n$, n≥1, presents cross-sectional and sequencial tail independence. We illustrate with examples of thinned multivariate time series and multivariate autoregressive processes with random coefficients. An application of these latter to financial data is presented at the end.
TypeArticle
URIhttp://hdl.handle.net/1822/36655
DOI10.1016/j.jmva.2015.02.002
ISSN0047-259X
Publisher versionhttp://www.sciencedirect.com/science/article/pii/S0047259X15000378
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

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