Vine copulas as a way to describe and analyze multi-variate dependence in econometrics: Computational motivation and comparison with Bayesian networks and fuzzy approaches

• Main Authors: Songsak Sriboonchitta, Jianxu Liu, Vladik Kreinovich, Hung T. Nguyen
• Format: Book Series
• Published: 2018
• Subjects: Computer Science; Engineering
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84897859595&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/53434
• Institution: Chiang Mai University

In the last decade, vine copulas emerged as a new efficient techniques for describing and analyzing multi-variate dependence in econometrics; see, e.g., [1, 2, 3, 7, 9, 10, 11, 13, 14, 21]. Our experience has shown, however, that while these techniques have been successfully applied to many practical problems of econometrics, there is still a lot of confusion and misunderstanding related to vine copulas. In this paper, we provide a motivation for this new technique from the computational viewpoint. We show that other techniques used to described dependence - Bayesian networks and fuzzy techniques - can be viewed as a particular case of vine copulas. © Springer International Publishing Switzerland 2014.