Multivariate Reflection Symmetry of Copula Functions

Monica Billio, Lorenzo Frattarolo () and Dominique Guegan ()
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Lorenzo Frattarolo: University of Ca’ Foscari [Venice, Italy]
Dominique Guegan: CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Labex ReFi - UP1 - Université Paris 1 Panthéon-Sorbonne

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Abstract: We propose a multivariate nonparametric copula test of reflection symmetry. The test is valid in any number of dimensions, extending previous results that cover the bivariate case. Furthermore, the asymptotic theory for the test relies on recent results on the dependent multiplier bootstrap, valid for sub-exponentially strongly mixing data. Consequently to the introduction of those two features, the procedure is suitable for financial time series whose asymmetric dependence, in distressed periods, has already been documented elsewhere. We conduct an extensive simulation study of empirical size and power and provide several examples of applications. In particular, we investigate the use of the statistic as a financial stress indicator by comparing it with the CISS, the leading ECB indicator.

Keywords: Financial Stress; Radial Symmetry; Reflection Symmetry; Copula; Dependence Asymmetry; Empirical Process; Dependent Multiplier Bootstrap (search for similar items in EconPapers)
Date: 2017-07
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01592147
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Published in 2017

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Related works:
Working Paper: Multivariate Reflection Symmetry of Copula Functions (2017)
Working Paper: Multivariate Reflection Symmetry of Copula Functions (2017)
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