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On the extremal behavior of a Pareto process: an alternative for ARMAX modeling
Kybernetika, Tome 48 (2012) no. 1, pp. 31-49 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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In what concerns extreme values modeling, heavy tailed autoregressive processes defined with the minimum or maximum operator have proved to be good alternatives to classical linear ARMA with heavy tailed marginals (Davis and Resnick [8], Ferreira and Canto e Castro [13]). In this paper we present a complete characterization of the tail behavior of the autoregressive Pareto process known as Yeh-Arnold-Robertson Pareto(III) (Yeh et al. [32]). We shall see that it is quite similar to the first order max-autoregressive ARMAX, but has a more robust parameter estimation procedure, being therefore more attractive for modeling purposes. Consistency and asymptotic normality of the presented estimators will also be stated.
In what concerns extreme values modeling, heavy tailed autoregressive processes defined with the minimum or maximum operator have proved to be good alternatives to classical linear ARMA with heavy tailed marginals (Davis and Resnick [8], Ferreira and Canto e Castro [13]). In this paper we present a complete characterization of the tail behavior of the autoregressive Pareto process known as Yeh-Arnold-Robertson Pareto(III) (Yeh et al. [32]). We shall see that it is quite similar to the first order max-autoregressive ARMAX, but has a more robust parameter estimation procedure, being therefore more attractive for modeling purposes. Consistency and asymptotic normality of the presented estimators will also be stated.
Classification : 60G70, 60J20
Keywords: extreme value theory; Markov chains; autoregressive processes; tail dependence 
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Ferreira, Marta. On the extremal behavior of a Pareto process: an alternative for ARMAX modeling. Kybernetika, Tome 48 (2012) no. 1, pp. 31-49. http://geodesic.mathdoc.fr/item/KYB_2012_48_1_a2/

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