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

Jong Min Kim, Hojin Jung

    Research output: Contribution to journalArticle

    4 Scopus citations

    Abstract

    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
    Volume50
    Issue number41
    DOIs
    StatePublished - Sep 2 2018

    Keywords

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

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