Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/195890 
Year of Publication: 
2017
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 5 [Issue:] 3 [Publisher:] MDPI [Place:] Basel [Year:] 2017 [Pages:] 1-12
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
extreme value theory
autoregressive processes
extremal index
asymptotic tail independence
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Appears in Collections:

Files in This Item:
File
Size
594.84 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.