Vine copula Granger causality in quantiles

Hyuna Jang, Jong Min Kim, Hohsuk Noh

Research output: Contribution to journalArticlepeer-review


Causal relationships between time series are typically examined by testing for Granger causality. Although many studies have tested Granger causality in mean, non-causality in mean does not need to carry over to other distribution characteristics or different parts of the distribution. This scenario has motivated many researchers to investigate causal relations from the perspective of conditional quantiles. Several methods have been proposed for both parametric and nonparametric modelling frameworks. Parametric methods have limitations in detecting nonlinear causality, whereas nonparametric methods have difficulty selecting smoothing parameters that significantly affect detection performance. To overcome the difficulties of both parametric and nonparametric Granger causality tests in quantiles, we propose a vine copula Granger causality test in quantiles using the semiparametric time-series modelling technique. The proposed test overcomes shortcomings in parametric modelling and has a computational advantage over nonparametric tests. Our test shows good performance in terms of size and power when using various simulated data. Finally, we illustrate our test using recent cryptocurrency data.

Original languageEnglish (US)
Pages (from-to)1109-1118
Number of pages10
JournalApplied Economics
Issue number10
StatePublished - 2024

Bibliographical note

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© 2023 Informa UK Limited, trading as Taylor & Francis Group.


  • Conditional quantiles
  • Granger causality
  • multivariate time-series
  • semiparametric modelling
  • stationary vine copula models


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