On tests for symmetry and radial symmetry of bivariate copulas towards testing for ellipticity

Miriam Jaser () and Aleksey Min
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Miriam Jaser: Technical University of Munich
Aleksey Min: Technical University of Munich

Computational Statistics, 2021, vol. 36, issue 3, No 15, 26 pages

Abstract: Abstract Very simple non-parametric tests are proposed to detect symmetry and radial symmetry in the dependence structure of bivariate copula data. The performance of the proposed tests is illustrated in an intensive simulation study and compared to the one of similar more advanced tests, which do not require known margins. Further, a powerful non-parametric testing procedure to decide whether the dependence structure of the underlying bivariate copula data may be captured by an elliptical copula is provided. The testing procedure makes use of intrinsic properties of bivariate elliptical copulas such as symmetry, radial symmetry, and equality of Kendall’s tau and Blomqvist’s beta. The proposed tests as well as the testing procedure are very simple to use in applications. For an illustration of the testing procedure for ellipticity, financial and insurance data is analyzed.

Keywords: Asymptotic normality; Elliptical copulas; Goodness-of-fit test; Kendall’s tau; Non-parametric tests; U-statistics (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00180-020-00994-0

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