# Stationary vine copula models for multivariate time series

@article{Nagler2020StationaryVC, title={Stationary vine copula models for multivariate time series}, author={Thomas Nagler and Daniel Kruger and Aleksey Min}, journal={arXiv: Methodology}, year={2020} }

Multivariate time series exhibit two types of dependence: across variables and across time points. Vine copulas are graphical models for the dependence and can conveniently capture both types of dependence in the same model. We derive the maximal class of graph structures that guarantees stationarity under a condition called translation invariance. Translation invariance is not only a necessary condition for stationarity, but also the only condition we can reasonably check in practice. In this… Expand

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