Flexible dynamic vine copula models for multivariate time series data

Elif F. Acar, Claudia Czado and Martin Lysy

Econometrics and Statistics, 2019, vol. 12, issue C, 181-197

Abstract: The representation of temporal patterns is essential to time series analysis. In the case of two or more time series, one needs to account for temporal patterns not only in each univariate series but also in their joint behavior. A multivariate model is proposed here for the specification of time-varying dependence patterns in multivariate time series in a flexible way. The model is built by first addressing the temporal patterns in each series and then modeling the interdependencies among their innovations using a time-varying vine copula model. To specify the vine decomposition, a heuristic model selection tool that accounts for both the magnitude and variation of the empirical Kendall tau across different time intervals is employed. The time variation in the strength of pairwise dependencies is inferred using nonparametric smoothing techniques, and the uncertainty in the resulting estimates is assessed using a parametric bootstrap. The methods are evaluated in a simulation study and used to analyze daily exchange rate returns of seven major currencies from August 2005 to August 2016.

Keywords: Dynamic vines; Exchange rate dependence; Kendall’s tau; Local likelihood; Multivariate time series (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:12:y:2019:i:c:p:181-197

DOI: 10.1016/j.ecosta.2019.03.002

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