In this paper we present a forecasting method for time series using copula-based models for multivariate time series. We study how the performance of the predictions evolves when changing the strength of the different possible dependencies and compare it with a univariate version of our forecasting method introduced recently by Sokolinskiy & Van Dijk. Moreover, we also study the influence of the marginal distribution with the help of a new performance measure and lastly we look at the impact of the dependence structure on the predictions performance. We also give an example of practical implementation with financial data.
Paru en novembre 2013 , 20 pages