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Weighted approximations of tail copula processes with application to testing the multivariate extreme value condition

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Author Info
Einmahl, J.H.J.
Haan, L. de
Li, D. (Tilburg University, Center for Economic Research)

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Abstract

Consider n i.i.d. random vectors on R2, with unknown, common distribution function F. Under a sharpening of the extreme value condition on F, we derive a weighted approximation of the corresponding tail copula process. Then we construct a test to check whether the extreme value condition holds by comparing two estimators of the limiting extreme value distribution, one obtained from the tail copula process and the other obtained by first estimating the spectral measure which is then used as a building block for the limiting extreme value distribution. We derive the limiting distribution of the test statistic from the aforementioned weighted approximation. This limiting distribution contains unknown functional parameters. Therefore we show that a version with estimated parameters converges weakly to the true limiting distribution. Based on this result, the finite sample properties of our testing procedure are investigated through a simulation study. A real data application is also presented.

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Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 71.

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Date of creation: 2004
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Handle: RePEc:dgr:kubcen:200471

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Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods

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  1. J.H.J. Einmahl & L.F.M. De Haan, 1998. "Nonparametric estimation of the spectral measure of an extreme value distribution," Econometric Institute Report 1998, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
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  1. Einmahl, John H.J. & Li, Jun & Liu, Regina Y., 2006. "Extreme value theory approach to simultaneous monitoring and tresholding of multiple risk indicators," Discussion Paper 104, Tilburg University, Center for Economic Research. [Downloadable!]
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