Local Gaussian copula inference with structural breaks: testing dependence predictability
🤖 AI Summary
This study investigates the predictability of dependence structures in conditional copulas while accounting for structural breaks in time series. To this end, the authors propose a robust score-type test that does not require prespecifying a parametric copula family. By integrating distributional regression with a local Gaussian copula approximation, the method flexibly captures dynamic dependence patterns and accommodates complex marginal dynamics. The approach is formulated within a semiparametric framework and employs a multi-stage estimator, with inference conducted via the moving block bootstrap. The asymptotic distribution of the test statistic is rigorously derived, and both Monte Carlo simulations and empirical analyses demonstrate its strong finite-sample performance.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
dependence predictability
structural breaks
conditional copulas
temporal instabilities
copula inference
Innovation
Methods, ideas, or system contributions that make the work stand out.
local Gaussian copula
structural breaks
dependence predictability
distributional regression
moving block bootstrap
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