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BY 4.0 license Open Access Published by De Gruyter Open Access May 24, 2021

Multivariate radial symmetry of copula functions: finite sample comparison in the i.i.d case

Monica Billio, Lorenzo Frattarolo and Dominique Guégan
From the journal Dependence Modeling

Abstract

Given a d-dimensional random vector X = (X1, . . ., Xd), if the standard uniform vector U obtained by the component-wise probability integral transform (PIT) of X has the same distribution of its point reflection through the center of the unit hypercube, then X is said to have copula radial symmetry. We generalize to higher dimensions the bivariate test introduced in [11], using three different possibilities for estimating copula derivatives under the null. In a comprehensive simulation study, we assess the finite-sample properties of the resulting tests, comparing them with the finite-sample performance of the multivariate competitors introduced in [17] and [1].

MSC 2010: 62G10; 62G30; 62H05; 62H15

References

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Received: 2021-01-05
Accepted: 2021-04-07
Published Online: 2021-05-24

© 2021 Monica Billio et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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