A Characterization of a Subclass of Separate Ratio-Type Copulas

TL;DR

This paper explores the theoretical properties of Separate Ratio-Type Copulas, providing new conditions for their validity and extending their applicability in various fields like finance and machine learning.

Contribution

It generalizes a key theorem for separate copulas, introduces new validity conditions, and broadens their theoretical framework and practical relevance.

Findings

Revised theorem characterizing copula validity

New assumptions ensuring copula validity

Extended applicability to finance and machine learning

Abstract

Copulas are essential tools in statistics and probability theory, enabling the study of the dependence structure between random variables independently of their marginal distributions. Among the various types of copulas, Ratio-Type Copulas have gained significant attention due to their flexibility in modeling joint distributions. This paper focuses on Separate Ratio-Type Copulas, where the dependence function is a separate product of univariate functions. We revisit a theorem characterizing the validity of these copulas under certain assumptions, generalize it to broader settings, and examine the conditions for reversing the theorem in the case of concave generating functions. To address its limitations, we propose new assumptions that ensure the validity of separate copulas under specific conditions. These results refine the theoretical framework for separate copulas, extending their applicability to pure mathematics and applied fields such as finance, risk management, and machine learning.