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). https://www.mdpi.com/2227-9091/9/1/14
Bladt, M., & McNeil, A.J. (2021). Time series copula models using d-vines and v-transforms. Econometrics and Statistics. https://www.sciencedirect.com/science/article/pii/S2452306221000800
Bladt, M., & McNeil, A.J. (2022). Time series models with infinite-order partial copula dependence. https://www.degruyter.com/document/doi/10.1515/demo-2022-0105/html
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.