Multivariate Skew Normal Copula for Asymmetric Dependence: Estimation and Application
- Authors
- Wei, Zheng; Kim, Seongyong; Choi, Boseung; Kim, Daeyoung
- Issue Date
- 1월-2019
- Publisher
- WORLD SCIENTIFIC PUBL CO PTE LTD
- Keywords
- Copula; non-exchangeability; radial asymmetry; skew-normal distribution
- Citation
- INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING, v.18, no.1, pp.365 - 387
- Indexed
- SCIE
SCOPUS
- Journal Title
- INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
- Volume
- 18
- Number
- 1
- Start Page
- 365
- End Page
- 387
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/68842
- DOI
- 10.1142/S021962201750047X
- ISSN
- 0219-6220
- 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - Graduate School > Department of Economics and Statistics > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.