# Dependence properties and Bayesian inference for asymmetric multivariate copulas

## TL;DR

This paper explores asymmetric copulas, providing new theoretical insights, dependence properties, and an iterative Bayesian inference method, with applications demonstrated through simulations.

## Contribution

It introduces a new iterative representation for Liebscher copulas that ensures uniform margins and facilitates Bayesian inference.

## Key findings

- Derived exact tail dependence expressions.
- Characterized copulas with arbitrary singular components.
- Developed an ABC sampling scheme for inference.

## Abstract

We study a broad class of asymmetric copulas introduced by Liebscher (2008) as a combination of multiple - usually symmetric - copulas. The main thrust of the paper is to provide new theoretical properties including exact tail dependence expressions and stability properties. A subclass of Liebscher copulas obtained by combining Fr\'echet copulas is studied in more details. We establish further dependence properties for copulas of this class and show that they are characterized by an arbitrary number of singular components. Furthermore, we introduce a novel iterative representation for general Liebscher copulas which de facto insures uniform margins, thus relaxing a constraint of Liebscher's original construction. Besides, we show that this iterative construction proves useful for inference by developing an Approximate Bayesian computation sampling scheme. This inferential procedure is demonstrated on simulated data.