Tail dependence and smoothness of time series

Helena Ferreira () and Marta Ferreira ()
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Helena Ferreira: Universidade da Beira Interior (UBI)
Marta Ferreira: University of Minho

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2021, vol. 30, issue 1, No 12, 198-210

Abstract: Abstract 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 $$\theta $$ 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 $$\theta $$ 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 $$\lambda $$ 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 $$\lambda $$ λ , and its performance will be evaluated based on a simulation study. We illustrate with an application to financial series.

Keywords: Extreme values; Smoothness coefficient; Tail dependence coefficient; 60G70 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11749-020-00709-z

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