High-frequency volatility connectedness and time-frequency correlation among Chinese stock and major commodity markets around COVID-19

  • Received April 21, 2022;
    Accepted June 21, 2022;
    Published June 23, 2022
  • Author(s)
  • DOI
  • Article Info
    Volume 19 2022, Issue #2, pp. 260-273
  • Cited by
    7 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

This study examines the connectedness and time-frequency correlation of price volatility across the Chinese stock market and major commodity markets. This paper applies a DCC-GARCH-based volatility connectedness model and the cross-wavelet transform to examine the transmission of risk patterns in these markets before and during the COVID-19 outbreak, as well as the leading lag relationship and synergistic movements between different time domains. First, the findings of the DCC-GARCH connectedness model show dynamic total spillovers are stronger after the COVID-19 outbreak. Chinese stocks and corn have been net spillovers in the system throughout the sample period, but the Chinese market plays the role of a net receiver of volatility relative to other markets (net pairwise directional connectedness) in the system as a whole. In terms of wavelet results, there is some connection to the connectedness results, with all commodity markets, except soybeans and wheat, showing significant dependence on Chinese equities in the medium/long term following the COVID-19 outbreak. Secondly, the medium-to long-term frequency of the crude oil market and copper market are highly dependent on the Chinese stock market, especially after the COVID-19 outbreak. Meanwhile, the copper market is the main source of risk for the Chinese stock market, while the wheat market sends the least shocks to the Chinese stock market. The findings of this paper will have a direct impact on a number of important decisions made by investors and policymakers.

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    • Figure 1. Time series plots of seven markets
    • Figure 2. Dynamic total connectedness
    • Figure 3. Net directional connectedness
    • Figure 4. Net pairwise directional connectedness
    • Figure 5. The wavelet coherency between major commodity and Chinese stock markets
    • Conceptualization
      Hongjun Zeng, Ran Lu
    • Data curation
      Hongjun Zeng, Ran Lu
    • Formal Analysis
      Hongjun Zeng, Ran Lu
    • Investigation
      Hongjun Zeng, Ran Lu
    • Methodology
      Hongjun Zeng
    • Project administration
      Hongjun Zeng, Ran Lu
    • Resources
      Hongjun Zeng, Ran Lu
    • Software
      Hongjun Zeng
    • Supervision
      Hongjun Zeng, Ran Lu
    • Validation
      Hongjun Zeng
    • Visualization
      Hongjun Zeng, Ran Lu
    • Writing – original draft
      Hongjun Zeng, Ran Lu
    • Writing – review & editing
      Hongjun Zeng, Ran Lu