Please use this identifier to cite or link to this item: https://hdl.handle.net/1822/46971

TitleAnalyzing the Gaver-Lewis Pareto process under an extremal perspective
Author(s)Ferreira, Marta Susana
Ferreira, Helena
KeywordsExtreme value theory
Autoregressive processes
Extremal index
Asymptotic tail independence
Issue date2017
PublisherMDPI
JournalRisks
Abstract(s)Pareto processes are suitable to model stationary heavy-tailed data. Here, we consider the auto-regressive Gaver–Lewis Pareto Process and address a study of the tail behavior. We characterize its local and long-range dependence. We will see that consecutive observations are asymptotically tail independent, a feature that is often misevaluated by the most common extremal models and with strong relevance to the tail inference. This also reveals clustering at “penultimate” levels. Linear correlation may not exist in a heavy-tailed context and an alternative diagnostic tool will be presented. The derived properties relate to the auto-regressive parameter of the process and will provide estimators. A comparison of the proposals is conducted through simulation and an application to a real dataset illustrates the procedure.
TypeArticle
URIhttps://hdl.handle.net/1822/46971
DOI10.3390/risks5030033
ISSN2227-9091
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

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