Directional time-varying partial correlation with the Gaussian copula–DCC–GARCH model

Jong Min Kim, Hojin Jung

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


This article suggests a directional time-varying partial correlation based on the dynamic conditional correlation (DCC) method. A recent study proposed the copula DCC based on the vine structure. Due to the arbitrary variable selection, their method can produce unnecessary dependence in the multivariate structure, with extra economic and computational burdens. To overcome this limitation, we incorporate directional dependence by copula to track the causal relationship among multiple variables and then extend the copula bivariate DCC method to a directional time varying partial correlation in the multivariate structure. Our proposed method provides a reasonable and efficient conditional dependence structure, without the trial and error process. We offer an application of our method to the U.S. stock market as an illustrated example.

Original languageEnglish (US)
Pages (from-to)4418-4426
Number of pages9
JournalApplied Economics
Issue number41
StatePublished - Sep 2 2018


  • Directional dependence
  • dynamic conditional correlation
  • time-varying partial correlation


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