Using a priori knowledge to construct copulas

TL;DR

This paper develops asymmetric copulas incorporating prior knowledge about variable dependence, especially in cases with order relations, and applies them to sea state data for improved modeling.

Contribution

It introduces a method to construct asymmetric copulas with minimal and maximal dependence features using mixture variables, tailored to prior knowledge about variable relations.

Findings

Models fit sea state data effectively

Likelihood ratio tests compare nested models

BIC criterion used for non-nested models

Abstract

Our purpose is to model the dependence between two random variables, taking into account a priori knowledge on these variables. For example, in many applications (oceanography, finance...), there exists an order relation between the two variables; when one takes high values, the other cannot take low values, but the contrary is possible. The dependence for the high values of the two variables is, therefore, not symmetric. However a minimal dependence also exists: low values of one variable are associated with low values of the other variable. The dependence can also be extreme for the maxima or the minima of the two variables. In this paper, we construct step by step asymmetric copulas with asymptotic minimal dependence, and with or without asymptotic maximal dependence, using mixture variables to get at first asymmetric dependence and then minimal dependence. We fit these models to a real dataset of sea states and compare them using Likelihood Ratio Tests when they are nested, and BIC- criterion (Bayesian Information criterion) otherwise.