Corpus ID: 221112123

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
Time series models with infinite-order partial copula dependence
Stationary and ergodic time series can be constructed using an s-vine decomposition based on sets of bivariate copula functions. The extension of such processes to infinite copula sequences isExpand
Stock market returns and oil price shocks: A CoVaR analysis based on dynamic vine copula models
TLDR
The results show that the early stages of the Covid-19 crisis lead to increasing risk levels in the BRICS stock markets except for the Chinese one, which has recovered quickly and therefore shows no changes in the risk level. Expand
Extreme event propagation using counterfactual theory and vine copulas
Understanding multivariate extreme events play a crucial role in managing the risks of complex systems since extremes are governed by their own mechanisms. Conditional on a given variable exceeding aExpand
Implicit Copulas: An Overview
  • Michael Stanley Smith
  • Mathematics, Economics
  • 2021
Implicit copulas are the most common copula choice for modeling dependence in high dimensions. This broad class of copulas is introduced and surveyed, including elliptical copulas, skew t copulas,Expand
Time series copula models using d-vines and v-transforms
An approach to modelling volatile financial return series using d-vine copulas combined with uniformity preserving transformations known as v-transforms is proposed. By generalizing the concept ofExpand

References

SHOWING 1-10 OF 87 REFERENCES
Copula Modelling of Dependence in Multivariate Time Series
Almost all existing nonlinear multivariate time series models remain linear, conditional on a point in time or latent regime. Here, an alternative is proposed, where nonlinear serial andExpand
Copula-based dynamic models for multivariate time series
TLDR
An intuitive way to couple several dynamic time series models even when there are no innovations is proposed and the results show that even if the univariate dynamic models depend on unknown parameters, the limiting behavior of many processes of interest does not depend on the estimation errors. Expand
General Vine Copula Models for Stationary Multivariate Time Series
Describing the serial, cross-serial and cross-sectional (conditional) dependence is animportant task in the analysis of multivariate time series. While the classical vectorautoregressive (VAR) modelExpand
COPAR-multivariate time series modeling using the copula autoregressive model
TLDR
A novel copula-based model is proposed that allows for the non-linear and non-symmetric modeling of serial as well as between-series dependencies and exploits the flexibility of vine copulas. Expand
Modeling Dependence in High Dimensions With Factor Copulas
This article presents flexible new models for the dependence structure, or copula, of economic variables based on a latent factor structure. The proposed models are particularly attractive forExpand
Estimation of Copula-Based Semiparametric Time Series Models
This paper studies the estimation of a class of copula-based semiparametric stationary Markov models. These models are characterized by nonparametric invariant (or marginal) distributions andExpand
Selecting and estimating regular vine copulae and application to financial returns
TLDR
It is shown how to evaluate the density of arbitrary regular vine specifications, which opens the vine copula methodology to the flexible modeling of complex dependencies even in larger dimensions. Expand
Copula-based semiparametric models for multivariate time series
TLDR
The authors extend to multivariate contexts the copula-based univariate time series modeling approach of Chen & Fan and discusses parameter estimation and goodness-of-fit testing for their model, with emphasis on meta-elliptical and Archimedean copulas. Expand
Forecasting Time Series with Multivariate Copulas
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 theExpand
TIME IRREVERSIBLE COPULA-BASED MARKOV MODELS
Economic and financial time series frequently exhibit time irreversible dynamics. For instance, there is considerable evidence of asymmetric fluctuations in many macroeconomic and financialExpand
...
1
2
3
4
5
...