Conditional Normal Extreme-Value Copulas

TL;DR

This paper introduces a new class of extreme-value copulas derived from conditional normal models, enabling flexible modeling of complex dependence structures in data such as spatial and factor-based data.

Contribution

It presents the development of a novel class of extreme-value copulas based on conditional normal models, along with estimation methods and practical applications.

Findings

Effective modeling of spatial and factor dependence structures.

Simulation results demonstrate the estimation methods' accuracy.

Application to wind and stock data shows practical utility.

Abstract

We propose a new class of extreme-value copulas which are extreme-value limits of conditional normal models. Conditional normal models are generalizations of conditional independence models, where the dependence among observed variables is modeled using one unobserved factor. Conditional on this factor, the distribution of these variables is given by the Gaussian copula. This structure allows one to build flexible and parsimonious models for data with complex dependence structures, such as data with spatial dependence or factor structure. We study the extreme-value limits of these models and show some interesting special cases of the proposed class of copulas. We develop estimation methods for the proposed models and conduct a simulation study to assess the performance of these algorithms. Finally, we apply these copula models to analyze data on monthly wind maxima and stock return minima.