A New Dependence Condition for Time Series and the Extremal Index of Higher-Order Markov Chains

We present a new dependence condition for time series and extend the extremal types theorem. The dependence structure of a stationary sequence is described by a sequence of extremal functions. Under a stability condition for the sequence of extremal functions, we obtain the asymptotic distribution...

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Main Author: Helena Ferreira
Format: Article
Language:English
Published: Instituto Nacional de Estatística | Statistics Portugal 2006-06-01
Series:Revstat Statistical Journal
Subjects:
Online Access:https://revstat.ine.pt/index.php/REVSTAT/article/view/32
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author Helena Ferreira
author_facet Helena Ferreira
author_sort Helena Ferreira
collection DOAJ
description We present a new dependence condition for time series and extend the extremal types theorem. The dependence structure of a stationary sequence is described by a sequence of extremal functions. Under a stability condition for the sequence of extremal functions, we obtain the asymptotic distribution of the sample maximum. As a corollary, we derive a surprisingly simple method for computing the extremal index through a limit of a sequence of extremal coefficients. The results may be used to determine the asymptotic distribution of extreme values from stationary time series based on copulas. We illustrate it with the study of the extremal behaviour of d th-order stationary Markov chains in discrete time with continuous state space. For such sequences we present a way to compute the extremal index from the upper extreme value limit for its joint distribution of d + 1 consecutive variables.
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spelling doaj.art-58accaed1d46414b9a46aa3cb469f3222022-12-22T01:28:34ZengInstituto Nacional de Estatística | Statistics PortugalRevstat Statistical Journal1645-67262183-03712006-06-014210.57805/revstat.v4i2.32A New Dependence Condition for Time Series and the Extremal Index of Higher-Order Markov ChainsHelena Ferreira 0University of Beira Interior We present a new dependence condition for time series and extend the extremal types theorem. The dependence structure of a stationary sequence is described by a sequence of extremal functions. Under a stability condition for the sequence of extremal functions, we obtain the asymptotic distribution of the sample maximum. As a corollary, we derive a surprisingly simple method for computing the extremal index through a limit of a sequence of extremal coefficients. The results may be used to determine the asymptotic distribution of extreme values from stationary time series based on copulas. We illustrate it with the study of the extremal behaviour of d th-order stationary Markov chains in discrete time with continuous state space. For such sequences we present a way to compute the extremal index from the upper extreme value limit for its joint distribution of d + 1 consecutive variables. https://revstat.ine.pt/index.php/REVSTAT/article/view/32extremal coefficientdependenceextremesextremal indexhigher-order stationary Markov sequences
spellingShingle Helena Ferreira
A New Dependence Condition for Time Series and the Extremal Index of Higher-Order Markov Chains
Revstat Statistical Journal
extremal coefficient
dependence
extremes
extremal index
higher-order stationary Markov sequences
title A New Dependence Condition for Time Series and the Extremal Index of Higher-Order Markov Chains
title_full A New Dependence Condition for Time Series and the Extremal Index of Higher-Order Markov Chains
title_fullStr A New Dependence Condition for Time Series and the Extremal Index of Higher-Order Markov Chains
title_full_unstemmed A New Dependence Condition for Time Series and the Extremal Index of Higher-Order Markov Chains
title_short A New Dependence Condition for Time Series and the Extremal Index of Higher-Order Markov Chains
title_sort new dependence condition for time series and the extremal index of higher order markov chains
topic extremal coefficient
dependence
extremes
extremal index
higher-order stationary Markov sequences
url https://revstat.ine.pt/index.php/REVSTAT/article/view/32
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