Time Series Copula Models

This package contains functions for the analysis of time series using copula models. A full description of a strictly stationary time series can be obtained by choosing a univariate marginal distribution and a time series copula process, i.e. a serially dependent process of uniform random variables. Examples of the latter are the copula processes of Gaussian ARMA models and D-vine copula processes. Methods are provided for simulation, estimation and forecasting of time series copula models.


McNeil, A.J. (2021). Modelling volatile time series with v-transforms and copulas. Risks, 9(14).

Bladt, M., & McNeil, A.J. (2021). Time series copula models using d-vines and v-transforms. Econometrics and Statistics.

Bladt, M., & McNeil, A.J. (2021). Time series models with infinite-order partial copula dependence.

Try the tscopula package in your browser

Any scripts or data that you put into this service are public.

tscopula documentation built on May 7, 2022, 5:06 p.m.