High-level dependence in time series models

被引：12

作者：

Fasen, Vicky ^{[1
]}

Klueppelberg, Claudia ^{[1
]}

Schlather, Martin ^{[2
]}

机构：

[1] Tech Univ Munich, Ctr Math Sci, D-85747 Garching, Germany

[2] Univ Gottingen, Ctr Stat, Gottingen, Germany

来源：

EXTREMES | 2010年 / 13卷 / 01期

关键词：

ARCH; COGARCH; Extreme cluster; Extreme dependence measure; Extremal index; Extreme value theory; GARCH; Linear model; Multivariate regular variation; Nonlinear model; Levy-driven Ornstein-Uhlenbeck process; Random recurrence equation; EXTREMAL BEHAVIOR; RANDOM-VARIABLES; AUTOREGRESSIVE PROCESS; MOVING AVERAGES; LIMIT THEORY; STATIONARITY; MULTIVARIATE; TAIL; DRIVEN;

D O I：

10.1007/s10687-009-0084-8

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

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.

引用

页码：1 / 33

页数：33

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