Dynamic Vine Copulas: Detecting and Quantifying Time-Varying Higher-Order Interactions
Time-varying dependence is often modeled with dynamic correlations or Gaussian graphical models, but multivariate systems can change through tail behavior, asymmetry, or conditional structure even when correlations are nearly stable. We introduce Dynamic Vine Copulas (DVC), a temporal vine-copula framework for estimating and diagnosing sequence-wide non-Gaussian dependence. DVC fixes a chosen vine factorization for comparability; the framework applies to C-, D-, and R-vines, and our experiments use fixed-root-order C-vines. Pair-copula states evolve through smooth parameter trajectories or temporally regularized family-switching paths. The main diagnostic is a held-out comparison between ...
| Authors | Houman Safaai, Alessandro Marin Vargas |
|---|---|
| Primary category | stat.ML |
| Published | 4 May 2026 |
| Last updated at source | 7 May 2026 |
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