Estimation and Inference in Factor Copula Models with Exogenous Covariates

TL;DR

This paper introduces a factor copula model incorporating exogenous covariates, providing consistent estimation and inference methods validated through theory, simulations, and an application to stock return dependence.

Contribution

It develops a novel factor copula model with exogenous factors and establishes its estimation and inference properties using a simulated methods of moments approach.

Findings

Estimator is consistent and asymptotically normal.

Bootstrap standard errors are valid.

Model effectively explains stock return dependence.

Abstract

A factor copula model is proposed in which factors are either simulable or estimable from exogenous information. Point estimation and inference are based on a simulated methods of moments (SMM) approach with non-overlapping simulation draws. Consistency and limiting normality of the estimator is established and the validity of bootstrap standard errors is shown. Doing so, previous results from the literature are verified under low-level conditions imposed on the individual components of the factor structure. Monte Carlo evidence confirms the accuracy of the asymptotic theory in finite samples and an empirical application illustrates the usefulness of the model to explain the cross-sectional dependence between stock returns.