Semi-parametric copula-based models under non-stationarity

Abstract

In this paper, we consider non-stationary random vectors, where the marginal distributions and the associated copula may be time-dependent. We propose estimators for the unknown parameters and we establish the limiting distribution of the estimators of the copula and the conditional copula, together with a parametric bootstrap method for constructing confidence bands around the estimator and for testing the adequacy of the model. We also consider three examples of functionals of the copula-based model under non-stationarity: conditional quantiles, conditional means, and conditional expected shortfalls. The asymptotic distribution of the estimation errors is shown to be Gaussian, and bootstrapping methods are proposed to estimate their asymptotic variances. The finite-sample performance of our estimators is investigated through Monte Carlo experiments, and we give three examples of implementation of the proposed methodology.