A two-component copula with links to insurance

TL;DR

This paper introduces a novel two-component copula model for insurance dependencies, capturing macro and micro factors affecting losses, expanding the tools available for risk modeling in insurance.

Contribution

It proposes a new non-Archimedean copula that models insurance losses influenced by combined macro and micro factors, enhancing dependency modeling capabilities.

Findings

The copula effectively captures complex dependencies between insurance entities.

It extends existing copula models beyond Archimedean types.

The model is applicable to diverse insurance loss scenarios.

Abstract

This paper presents a new copula to model dependencies between insurance entities, by considering how insurance entities are affected by both macro and micro factors. The model used to build the copula assumes that the insurance losses of two companies or lines of business are related through a random common loss factor which is then multiplied by an individual random company factor to get the total loss amounts. The new two-component copula is not Archimedean and it extends the toolkit of copulas for the insurance industry.