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Regularly varying multivariate time series (CROSBI ID 152589)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Basrak, Bojan ; Segers, Johan Regularly varying multivariate time series // Stochastic processes and their applications, 119 (2009), 4; 1055-1080. doi: 10.1016/j.spa.2008.05.004

Podaci o odgovornosti

Basrak, Bojan ; Segers, Johan

engleski

Regularly varying multivariate time series

Extreme values of a stationary, multivariate time series may exhibit dependence across coordinates and over time. The aim of this paper is to offer a new and po- tentially useful tool called tail process to describe and model such extremes. The key property is the following fact: existence of the tail process is equivalent to mul- tivariate regular variation of finite cuts of the original process. Certain remarkable properties of the tail process are exploited to shed new light on known results on certain point processes of extremes. The theory is shown to be applicable with great ease to stationary solutions of stochastic autoregressive processes with random coef- ficient matrices, an interesting special case being a recently proposed factor GARCH model. In this class of models, the distribution of the tail process is calculated by a combination of analytical methods and a novel sampling algorithm.

autoregressive process ; clusters of extremes ; extremal index ; factor

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Podaci o izdanju

119 (4)

2009.

1055-1080

objavljeno

0304-4149

1879-209X

10.1016/j.spa.2008.05.004

Povezanost rada

Matematika

Poveznice
Indeksiranost