Local Gaussian copula inference with structural breaks: testing dependence predictability

Fuente: arXiv
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Main Authors: Mayer, Alexander, Oka, Tatsushi, Wied, Dominik
Format: Preprint
Published: 2026
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author Mayer, Alexander
Oka, Tatsushi
Wied, Dominik
author_facet Mayer, Alexander
Oka, Tatsushi
Wied, Dominik
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2603_01721
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Local Gaussian copula inference with structural breaks: testing dependence predictability
Mayer, Alexander
Oka, Tatsushi
Wied, Dominik
Econometrics
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.
title Local Gaussian copula inference with structural breaks: testing dependence predictability
topic Econometrics
url https://arxiv.org/abs/2603.01721