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A Time-Varying Multivariate Noncentral Contaminated Normal Copula Model and Its Application to the Visualized Dependence Analysis of Hong Kong Stock Markets

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  • Zhenyu Xiao
  • Jie Wang
  • Teng Yuan Cheng
  • Kuiran Shi

Abstract

Financial data usually have the features of complexity and interdependence structure, such as asymmetric, tail, and time-varying dependence. This study constructs a new multivariate skewed fat-tailed copula, namely, noncentral contaminated normal (NCCN) copula, to analyze the dependent structure of financial market data. The dynamic conditional correlation (DCC) model is also incorporated into constructing the time-varying NCCN copula model. This study comprehensively examines the effects of the DCC-NCCN copula and related models on fitting dependence structures of Hong Kong stock markets. The results show that the DCC-NCCN copula model can better depict the dependence structures of returns. Considering the flexibility and complexity, the DCC-NCCN copula model is a relatively ideal, time-varying, multivariate skewed fat-tailed copula model.

Suggested Citation

  • Zhenyu Xiao & Jie Wang & Teng Yuan Cheng & Kuiran Shi, 2020. "A Time-Varying Multivariate Noncentral Contaminated Normal Copula Model and Its Application to the Visualized Dependence Analysis of Hong Kong Stock Markets," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-23, October.
    • Handle: RePEc:hin:jnddns:9673623
    DOI: 10.1155/2020/9673623
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