Modelling volatility dependence with score copula models

Abstract I study score-driven models for modelling high persistence dependence between financial volatility series. I model this persistence dependence with two components, one for the long memory and the other for the short-term process. The addition of components offers a parsimonious solution for modelling high persistence and also allows for a short-term component for the transient shocks. I apply the model to emerging equities in the Americas. The estimates are robust to the advent of the pandemic. In addition, data resampling and marginal alternatives deliver similar parameter estimates. The proposed two-component model improves the in-sample diagnostics and generates more accurate out-of-sample forecasts.