Local Gaussian copula inference with structural breaks: testing dependence predictability

TL;DR

This paper introduces a robust score test for dependence predictability in conditional copulas, accommodating structural breaks and flexible marginal dynamics, with proven theoretical properties and demonstrated effectiveness through simulations and real data.

Contribution

It presents a novel semiparametric test for dependence predictability that is robust to structural breaks and does not require specifying the copula family.

Findings

Test maintains good size and power in simulations.

Method effectively detects dependence changes in empirical data.

Resampling scheme provides reliable inference.

Abstract

We propose a score test for dependence predictability in conditional copulas that is robust to temporal instabilities. Our semiparametric procedure accommodates flexible dynamics in the marginal processes and remains agnostic about the copula family by leveraging distributional regression techniques together with a local Gaussian representation of the copula link function. We derive the limiting distribution of our test statistic and propose a resampling scheme based on recent results for the moving block bootstrap of multi-stage estimators. Monte Carlo simulations and an empirical application illustrate the finite-sample performance of our methods.