High-level dependence in time series models

Fasen, Vicky ; Klüppelberg, Claudia ; Schlather, Martin

DOI: https://doi.org/10.1007/s10687-009-0084-8
URL: https://link.springer.com/article/10.1007/s10687-0...
Additional URL: https://www.semanticscholar.org/paper/High-level-d...
Document Type: Article
Year of publication: 2010
The title of a journal, publication series: Extremes : Statistical Theory and Applications in Science, Engineering and Economics
Volume: 13
Issue number: 1
Page range: 1-33
Place of publication: Dordrecht [u.a.]
Publishing house: Springer Science + Business Media
ISSN: 1386-1999 , 1572-915X
Publication language: English
Institution: School of Business Informatics and Mathematics > Mathematische Statistik (Schlather 2012-)
Subject: 510 Mathematics
Keywords (English): ARCH, COGARCH, Extreme cluster, Extreme dependence measure, Extremal index, Extreme value theory, GARCH, Linear model, Multivariate regular variation, Nonlinear model, Lévy-driven Ornstein–Uhlenbeck process, Random recurrence equation
Abstract: We present several notions of high-level dependence for stochastic processes, which have appeared in the literature. We calculate such measures for discrete and continuous-time models, where we concentrate on time series with heavy-tailed marginals, where extremes are likely to occur in clusters. Such models include linear models and solutions to random recurrence equations; in particular, discrete and continuous-time moving average and (G)ARCH processes. To illustrate our results we present a small simulation study.

Dieser Datensatz wurde nicht während einer Tätigkeit an der Universität Mannheim veröffentlicht, dies ist eine Externe Publikation.

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