Vine copula Granger causality in quantiles

ABSTRACT

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.

KEYWORDS:

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

JEL CLASSIFICATION:

• C14
• C22
• C53

Highlights

• We propose a nonlinear Granger causality test in quantiles using stationary vine copula models.

• Our test has computational advantages over nonparametric Granger causality tests.

• Our test has a simpler bootstrap approximation of the null distribution of the test statistic.

• Our test needs no smoothing parameter selection.

• Our test has competitive performance vis-a-vis previous Granger causality tests in quantiles.

Acknowledgments

The authors are indebted to the reviewer and the Editor for their excellent comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/00036846.2023.2174941

Additional information

Funding

This research was supported by the Basic Research Program through the National Research Foundation of Korea, funded by the Ministry of Education [NRF-2017R1D1A1A09000804].