Multivariate Skew Normal Copula for Asymmetric Dependence: Estimation and Application

Zheng Wei (), Seongyong Kim (), Boseung Choi () and Daeyoung Kim
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Zheng Wei: Department of Mathematics and Statistics, University of Maine, Orono, Maine, 04469-5752, USA
Seongyong Kim: Department of Applied Statistics, Hoseo University, Asan-si, Chungcheongnam-do, 31499, Republic of Korea
Boseung Choi: Korea University Sejong Campus, Division of Economics and Statistics, Department of National Statistics, Sejong, 30019, Republic of Korea
Daeyoung Kim: Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts, 01003-9305, USA

International Journal of Information Technology & Decision Making (IJITDM), 2019, vol. 18, issue 01, 365-387

Abstract: The exchangeability and radial symmetry assumptions on the dependence structure of the multivariate data are restrictive in practical situations where the variables of interest are not likely to be associated to each other in an identical manner. In this paper, we propose a flexible class of multivariate skew normal copulas to model high-dimensional asymmetric dependence patterns. The proposed copulas have two sets of parameters capturing asymmetric dependence, one for association between the variables and the other for skewness of the variables. In order to efficiently estimate the two sets of parameters, we introduce the block coordinate ascent algorithm and discuss its convergence property. The proposed class of multivariate skew normal copulas is illustrated using a real data set.

Keywords: Copula; non-exchangeability; radial asymmetry; skew-normal distribution (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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DOI: 10.1142/S021962201750047X

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