A semiparametric estimation of copula models based on the method of moments

TL;DR

This paper introduces a new semiparametric method for estimating multi-parameter copula models using moments, demonstrating its efficiency and simplicity through theoretical properties and simulation comparisons.

Contribution

It develops a novel moment-based estimation procedure for copula models, with proven consistency and asymptotic normality, and compares favorably to existing methods.

Findings

Estimates are consistent and asymptotically normal.

The method is faster and simpler than alternative approaches.

Simulation shows reasonable bias and root mean squared error.

Abstract

Using the classical estimation method of moments, we propose a new semiparametric estimation procedure for multi-parameter copula models. Consistency and asymptotic normality of the obtained estimators are established. By considering an Archimedean copula model, an extensive simulation study, comparing these estimators with the pseudo maximum likelihood, rho-inversion and tau-inversion ones, is carried out. We show that, with regards to the other methods, the moment based estimation is quick and simple to use with reasonable bias and root mean squared error.