Adaptive estimation of the copula correlation matrix for semiparametric elliptical copulas

TL;DR

This paper develops adaptive, data-driven methods for estimating the copula correlation matrix in semi-parametric elliptical copula models, providing sharp bounds and oracle inequalities for the estimators.

Contribution

It introduces a refined estimator for the copula correlation matrix using low-rank plus diagonal matrix fitting with nuclear norm penalty, along with sharp bounds and oracle inequalities.

Findings

Sharp operator norm bounds for the plug-in estimator

Finite sample oracle inequalities for the refined estimator

Closed-form estimators for elementary factor copula models

Abstract

We study the adaptive estimation of copula correlation matrix $\Sigma$ for the semi-parametric elliptical copula model. In this context, the correlations are connected to Kendall's tau through a sine function transformation. Hence, a natural estimate for $\Sigma$ is the plug-in estimator $\hat{\Sigma}$ with Kendall's tau statistic. We first obtain a sharp bound on the operator norm of $\hat{\Sigma}-\Sigma$. Then we study a factor model of $\Sigma$, for which we propose a refined estimator $\widetilde{\Sigma}$ by fitting a low-rank matrix plus a diagonal matrix to $\hat{\Sigma}$ using least squares with a nuclear norm penalty on the low-rank matrix. The bound on the operator norm of $\hat{\Sigma}-\Sigma$ serves to scale the penalty term, and we obtain finite sample oracle inequalities for $\widetilde{\Sigma}$. We also consider an elementary factor copula model of $\Sigma$, for which we propose closed-form estimators. All of our estimation procedures are entirely data-driven.