Analysis of multivariate time series is a common problem in areas like
finance and economics.
The classical tool for this purpose are vector autoregressive models.
These however are limited to the modeling of linear and symmetric
We propose a novel copula-based model which allows for non-linear and
asymmetric modeling of serial as well as between-series dependencies.
The model exploits the flexibility of vine copulas which are built up by
bivariate copulas only.
We descr... »