Vine copula Granger causality in mean

Hyuna Jang, Jong Min Kim, Hohsuk Noh

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Ever since the Granger causality test was proposed in 1969, financial market researchers have used it heavily to determine whether the past of a one-time series facilitates the future prediction of another time series. However, as many researchers have noted, the traditional Granger causality test based on the vector autoregression model has limitations in detecting nonlinear causality. To relax the parametric model assumptions of the Granger causality test, nonparametric versions have been proposed to use the advantage of detecting nonlinear Granger causality but have shown difficulty in selecting smoothing parameters that significantly affect detection performance. To overcome the difficulties of both parametric and nonparametric Granger causality tests, we propose the vine copula Granger causality test in mean based on the semiparametric time-series modeling technique. The proposed test overcomes the shortcomings of parametric modeling and has a computational advantage over the nonparametric tests. Our test shows good size and power performance with various simulated data and a real data.

Original languageEnglish (US)
Article number105798
JournalEconomic Modelling
Volume109
DOIs
StatePublished - Apr 2022

Bibliographical note

Funding Information:
This work was supported by a grant from the National Research Foundation of Korea funded by the Ministry of Education (NRF-2017R1D1A1A09000804). The authors are indebted to two reviewers and the Editor for their excellent comments and suggestions. Any remaining errors are ours.

Funding Information:
This work was supported by a grant from the National Research Foundation of Korea funded by the Ministry of Education ( NRF-2017R1D1A1A09000804 ). The authors are indebted to two reviewers and the Editor for their excellent comments and suggestions. Any remaining errors are ours.

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • Granger causality
  • Multivariate time series
  • Semiparametric modeling
  • Stationary vine copula models

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