A latent trawl process model for extreme values

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Title: A latent trawl process model for extreme values
Authors: Noven, R
Veraart, A
Gandy, A
Item Type: Journal Article
Abstract: This paper presents a new model for characterising temporal dependence in exceedances above a threshold. The model is based on the class of trawl pro cesses, which are stationary, infinitely divisible stochastic processes. The model for ex treme values is constructed by embedding a trawl process in a hierarchical framework, whic h ensures that the marginal distribution is generalised Pareto, as expected from class ical extreme value theory. We also consider a modified version of this model that works with a wider class of generalised Pareto distributions, and has the advantage of separating m arginal and temporal depen- dence properties. The model is illustrated by applications to environmental time series, and it is shown that the model offers considerable flexibility in capturing the dependence structure of extreme value data
Issue Date: 11-Sep-2018
Date of Acceptance: 20-Mar-2018
URI: http://hdl.handle.net/10044/1/63333
DOI: https://dx.doi.org/10.21314/JEM.2018.179
ISSN: 1756-3607
Publisher: Incisive Media
Start Page: 1
End Page: 24
Journal / Book Title: Journal of Energy Markets
Volume: 11
Issue: 3
Copyright Statement: Copyright © Infopro Digital Limited 2018. All rights reserved. You may share using our article tools. This article may be printed for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions. https://www.infopro-insight.com/termsconditions/insight-subscriptions
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: FP7-PEOPLE-2012-CIG-321707
Keywords: Social Sciences
Economics
Business & Economics
trawl process
peaks over threshold
generalized Pareto distribution (GPD)
pairwise likelihood estimation
marginal transformation model
conditional tail dependence coefficient
INFINITELY DIVISIBLE PROCESSES
STATIONARY
STATISTICS
DEPENDENCE
stat.ME
stat.ME
Publication Status: Published
Embargo Date: 2019-09-11
Appears in Collections:Mathematics
Statistics
Faculty of Natural Sciences



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