Stationary vine copula models for multivariate time series

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 guarantee stationarity...

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Bibliographic Details
Published in:Journal of econometrics 2022-04, Vol.227 (2), p.305-324
Main Authors: Nagler, Thomas, Krüger, Daniel, Min, Aleksey
Format: Article
Language:English
Subjects:
Bootstrap
Dependence
Estimating techniques
Forecasting
Graphical models
Markov chain
Measurement
Multivariate analysis
Open source software
Pair-copula
Sequential maximum likelihood
Simulation
Time series
Translation
Uncertainty
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Summary: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 guarantee stationarity under a natural and verifiable condition called translation invariance. We propose computationally efficient methods for estimation, simulation, prediction, and uncertainty quantification and show their validity by asymptotic results and simulations. The theoretical results allow for misspecified models and, even when specialized to the iid case, go beyond what is available in the literature. The new model class is illustrated by an application to forecasting returns of a portfolio of 20 stocks, where they show excellent forecast performance. The paper is accompanied by an open source software implementation.
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2021.11.015