Nonparametric Time-Varying Mixture Copula Models

Time-varying copula models have been shown to offer additional flexibility among multiple financial time series. In this paper we propose a new nonparametric time-varying mixture copula model, in which both weights and dependence parameters are deterministic functions of time. Asymptotic properties of the local constant estimators are established, and a revised expectation maximization (EM) algorithm is employed to reduce the computational burden. We use Monte Carlo simulation to examine the finite sample performance of the proposed estimators. In a financial example, we show that our proposed model outperforms the benchmark copula models.