# Bayesian model selection of vine copulas: a loss-based perspective

arXiv: https://arxiv.org/abs/2606.21512 · HTML: https://mlsanity.com/paper/2606.21512/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 19 Jun 2026.

## Builds on (most cited first, 15 of 15)

- [Model selection in sparse high-dimensional vine copula models with application to portfolio risk](https://mlsanity.com/paper/1801.09739/) (2018)
- [Simplified vine copula models: state of science and affairs](https://mlsanity.com/paper/2410.16806/) (2024)
- [Sequential Bayesian Model Selection of Regular Vine Copulas](https://mlsanity.com/paper/1512.00976/) (2015)
- [Selection of Sparse Vine Copulas in High Dimensions with the Lasso](https://mlsanity.com/paper/1705.05877/) (2017)
- [Model distances for vine copulas in high dimensions](https://mlsanity.com/paper/1510.03671/) (2015)
- [Representing sparse Gaussian DAGs as sparse R-vines allowing for non-Gaussian dependence](https://mlsanity.com/paper/1604.04202/) (2016)
- [Dependence Modeling in Ultra High Dimensions with Vine Copulas and the Graphical Lasso](https://mlsanity.com/paper/1709.05119/) (2017)
- [Bayesian Nonparametric Conditional Copula Estimation of Twin Data](https://mlsanity.com/paper/1603.03484/) (2016)
- [A Loss-Based Prior for Gaussian Graphical Models](https://mlsanity.com/paper/1812.05531/) (2018)
- [Bayesian Multivariate Nonlinear State Space Copula Models](https://mlsanity.com/paper/1911.00448/) (2019)
- [Bayesian Nonparametric Modelling of Conditional Multidimensional Dependence Structures](https://mlsanity.com/paper/2109.10969/) (2021)
- [Loss-based prior for CART and BART models](https://mlsanity.com/paper/2404.00359/) (2024)
- [Model selection tests for truncated vine copulas under nested hypotheses](https://mlsanity.com/paper/2501.13304/) (2025)
- [Throwing Vines at the Wall: Structure Learning via Random Search](https://mlsanity.com/paper/2510.20035/) (2025)
- [Trunc-Opt vine building algorithms](https://mlsanity.com/paper/2512.14399/) (2025)

## Abstract (arXiv)

The growing popularity of vine copulas in multivariate statistical analysis is largely driven by their ability to capture complex dependence structures. However, this flexibility comes at a cost, as the number of possible vine models grows rapidly and becomes intractable even in moderately low-dimensional settings. These limitations affect the practical applicability of current Bayesian inference and model selection approaches, effectively restricting it to problems of relatively small-dimension due to their high computational cost. This paper addresses the still open challenge of efficient model selection and estimation in Bayesian vine methodology. We propose a novel framework for Bayesian vine copula model selection that combines loss-based model priors with the shotgun stochastic search strategy. The strength of the proposed approach is twofold: it promotes sparsity and enables fast and effective structure selection. Furthermore, our comprehensive framework jointly identifies the vine structure, selects the copula families, and estimates the model parameters. The power of the proposed approach is demonstrated via simulation studies and an application to a real dataset of EFT portfolio asset returns.