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Local Gaussian copula inference with structural breaks: testing dependence predictability

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  • Alexander Mayer
  • Tatsushi Oka
  • Dominik Wied

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.

Suggested Citation

  • Alexander Mayer & Tatsushi Oka & Dominik Wied, 2026. "Local Gaussian copula inference with structural breaks: testing dependence predictability," Papers 2603.01721, arXiv.org.
    • Handle: RePEc:arx:papers:2603.01721
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    File URL: http://arxiv.org/pdf/2603.01721
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