Non-monotone dependence modeling with copulas: an application to the volume-return relationship

TL;DR

This paper presents a new parametric copula modeling approach for capturing complex non-monotone dependence structures, demonstrated through analyzing volume-return relationships in financial markets.

Contribution

It introduces an innovative method for constructing flexible copulas capable of modeling arbitrary non-monotone dependencies in data.

Findings

Enhanced modeling of non-monotone dependence structures

Application to financial volume-return data

Demonstrated superior adaptability over existing models

Abstract

This paper introduces an innovative method for constructing copula models capable of describing arbitrary non-monotone dependence structures. The proposed method enables the creation of such copulas in parametric form, thus allowing the resulting models to adapt to diverse and intricate real-world data patterns. We apply this novel methodology to analyze the relationship between returns and trading volumes in financial markets, a domain where the existence of non-monotone dependencies is well-documented in the existing literature. Our approach exhibits superior adaptability compared to other models which have previously been proposed in the literature, enabling a deeper understanding of the dependence structure among the considered variables.