Non-Parametric Estimation Techniques of Factor Copula Model using Proxies

TL;DR

This paper introduces a non-parametric kernel estimator for linking copulas in factor copula models, offering a flexible and consistent method for capturing complex dependencies in high-dimensional data.

Contribution

It presents a novel non-parametric estimation technique for factor copula models, improving accuracy over traditional parametric methods especially in complex dependency scenarios.

Findings

Estimator is consistent under mild conditions

Demonstrates superior performance in simulations

Effective in modeling complex dependencies

Abstract

Parametric factor copula models typically work well in modeling multivariate dependencies due to their flexibility and ability to capture complex dependency structures. However, accurately estimating the linking copulas within these models remains challenging, especially when working with high-dimensional data. This paper proposes a novel approach for estimating linking copulas based on a non-parametric kernel estimator. Unlike conventional parametric methods, our approach utilizes the flexibility of kernel density estimation to capture the underlying dependencies more accurately, particularly in scenarios where the underlying copula structure is complex or unknown. We show that the proposed estimator is consistent under mild conditions and demonstrate its effectiveness through extensive simulation studies. Our findings suggest that the proposed approach offers a promising avenue for modeling multivariate dependencies, particularly in applications requiring robust and efficient estimation of copula-based models.