Year: 2026
Type: preprint
Abstract: Traditional multivariate copulas often fail to capture complex dependence structures and may impose restrictive assumptions that limit their applicability to real-world data. To address these issues, vine copulas provide a flexible framework by constructing multivariate models from bivariate copulas. The three main types are D-vines, C-vines and R-vines. To simplify computations, the simplifying assumption is often applied, assuming conditional copulas are independent of conditioning variables. This results in some variables exerting minimal influence on the conditional copulas. Therefore, this study explores two subclasses of R-vines with node degrees capped at three, assessing its performance against existing models. The statistical results demonstrate that the proposed degree-constrained vine structures can serve as effective alternatives to existing vine copula models.
Source: arXiv (Cornell University)
Institution: Chulalongkorn University
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: green
Funder: Chulalongkorn University
OpenAlex ID: W7214388254