Tail dependence and smoothness of time series
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Helena Ferreira [1] ; Marta Ferreira [2]
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[1] Universidade da Beira Interior
Universidade da Beira Interior
Covilhã (Conceição), Portugal
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[2] Universidade de Lisboa
Universidade de Lisboa
Socorro, Portugal
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- Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 30, Nº. 1, 2021, págs. 198-210
- Idioma: inglés
- DOI: 10.1007/s11749-020-00709-z
- Texto completo no disponible (Saber más ...)
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Resumen
The risk of catastrophes is related to the possibility of occurring extreme values. Several statistical methodologies have been developed in order to evaluate the propensity of a process for the occurrence of high values and the permanence of these in time. The extremal index θ (Leadbetter in Z Wahrscheinlichkeitstheor Verw Geb 65:291–306, 1983) allows to infer the tendency for clustering of high values, but does not allow to evaluate the greater or less amount of oscillations in a cluster. The estimation of θ entails the validation of local dependence conditions regulating the distance between high levels oscillations of the process, which is difficult to implement in practice. In this work, we propose a smoothness coefficient to evaluate the degree of smoothness/oscillation in the trajectory of a process, with an intuitive reading and simple estimation. Application in some examples will be provided. We will see that, in a stationary sequence, it coincides with the tail dependence coefficient λ (Sibuya in Ann Inst Stat Math 11:195–210, 1960; Joe in Multivariate models and dependence concepts. Monographs on statistics and applied probability, vol 73. Chapman and Hall, London, 1997), providing a new interpretation of the latter. This relationship will inspire a new estimator for λ, and its performance will be evaluated based on a simulation study. We illustrate with an application to financial series.
