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Multivariate Skew Normal Copula for Asymmetric Dependence: Estimation and Application

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zheng Wei & Seongyong Kim & Boseung Choi & Daeyoung Kim, 2019. "Multivariate Skew Normal Copula for Asymmetric Dependence: Estimation and Application," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 365-387, January.
    • Handle: RePEc:wsi:ijitdm:v:18:y:2019:i:01:n:s021962201750047x
    DOI: 10.1142/S021962201750047X
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