A class of smooth, possibly data-adaptive nonparametric copula estimators containing the empirical beta copula

TL;DR

This paper introduces a broad class of smooth, data-adaptive nonparametric copula estimators, including the empirical beta copula, and identifies specific estimators that outperform existing methods in simulations.

Contribution

It proposes a new class of smooth copula estimators with data-adaptive parameters and demonstrates their superior performance over the empirical beta copula in Monte Carlo experiments.

Findings

Two data-adaptive estimators outperform the empirical beta copula.

Conditions for weak convergence of sequential empirical copula processes.

Empirical investigations highlight the influence of smoothing parameters.

Abstract

A broad class of smooth, possibly data-adaptive nonparametric copula estimators that contains empirical Bernstein copulas introduced by Sancetta and Satchell (and thus the empirical beta copula proposed by Segers, Sibuya and Tsukahara) is studied. Within this class, a subclass of estimators that depend on a scalar parameter determining the amount of marginal smoothing and a functional parameter controlling the shape of the smoothing region is specifically considered. Empirical investigations of the influence of these parameters suggest to focus on two particular data-adaptive smooth copula estimators that were found to be uniformly better than the empirical beta copula in all of the considered Monte Carlo experiments. Finally, with future applications to change-point detection in mind, conditions under which related sequential empirical copula processes converge weakly are provided.