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Tail Dependence of a Pareto Process

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New Advances in Statistical Modeling and Applications

Part of the book series: Studies in Theoretical and Applied Statistics ((STASSPSS))

Abstract

Heavy-tailed autoregressive processes defined with minimum or maximum operator are good alternatives to classic linear ARMA with heavy tail noises, in what concerns extreme values modeling. In this paper we present a full characterization of the tail dependence of the autoregressive minima process, Yeh–Arnold–Robertson Pareto(III).

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Author information

Authors and Affiliations

  1. University of Minho, DMA/CMAT, Braga, Portugal

    Marta Ferreira

Authors
  1. Marta Ferreira
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Correspondence to Marta Ferreira .

Editor information

Editors and Affiliations

  1. CEMAT and Departamento de Matemática, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    António Pacheco

  2. CEAUL and School of Technology and Management, Polytechnic Institute of Leiria, Leiria, Portugal

    Rui Santos

  3. CEMAT and Departamento de Matemática, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    Maria do Rosário Oliveira

  4. CEAUL and Departamento de Matemática, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    Carlos Daniel Paulino

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Ferreira, M. (2014). Tail Dependence of a Pareto Process. In: Pacheco, A., Santos, R., Oliveira, M., Paulino, C. (eds) New Advances in Statistical Modeling and Applications. Studies in Theoretical and Applied Statistics(). Springer, Cham. https://doi.org/10.1007/978-3-319-05323-3_17

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