Liquidity spillover from carbon emission trading markets to stock markets in China

  • Received September 5, 2023;
    Accepted November 10, 2023;
    Published November 16, 2023
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/imfi.20(4).2023.19
  • Article Info
    Volume 20 2023, Issue #4, pp. 227-241
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This work is licensed under a Creative Commons Attribution 4.0 International License

This study delves into China’s carbon emissions trading markets, investigating the interplay between carbon price liquidity and stock liquidity. Focusing on 338 companies listed in the national and eight pilot markets of the carbon emissions trading system from August 2013 to October 2023, the empirical finding reveals a positive impact of carbon price liquidity on stock liquidity. Notably, this positive association manifests more robustly in industries characterized by low carbon intensity compared to those with high carbon intensity, is more prominent during the COVID-19 period than in preceding times, and is particularly accentuated in the Hubei Province and Chongqing, as opposed to the remaining seven regions. Intriguingly, both carbon price liquidity and stock liquidity display positive autocorrelations in vector autoregression analysis. The endogeneity concern is alleviated by the two-stage least squares regressions, using lagged carbon price liquidity as instrumental variables. This study contributes to an enhanced comprehension of the dynamic interaction between carbon price liquidity and stock liquidity contextualized within China’s evolving carbon market landscape. The insights garnered herein hold substantial value for investors and government stakeholders seeking to navigate this evolving financial terrain.

Acknowledgment
This research was supported by the Summer Student Partnering with Faculty Research Program of Wenzhou-Kean University (WKUSSPF202304), the Wenzhou Association for Science and Technology – Service and Technology Innovation Program (jczc0254), and the Department of Education of Zhejiang Province – General Program (Y202353438).

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    • Figure 1. Vector autoregression impulse function
    • Table 1. Descriptive statistics
    • Table 2. Pairwise correlations between variables
    • Table 3. Baseline regression
    • Table 4. Low- and high-carbon-intensity industries
    • Table 5. Different ETS markets
    • Table 6. Subperiod analysis
    • Table 7. Vector autoregression analysis
    • Table 8. Two-stage least squares regression results
    • Data curation
      Xinyuan Yang, Jingyao Zhu, Hantao Xie, Jianing Zhang
    • Formal Analysis
      Xinyuan Yang, Jingyao Zhu, Hantao Xie, Jianing Zhang
    • Investigation
      Xinyuan Yang, Jingyao Zhu, Hantao Xie, Jianing Zhang
    • Methodology
      Xinyuan Yang, Jingyao Zhu, Hantao Xie, Jianing Zhang
    • Resources
      Xinyuan Yang, Jingyao Zhu, Hantao Xie
    • Software
      Xinyuan Yang, Hantao Xie
    • Validation
      Xinyuan Yang, Jingyao Zhu, Jianing Zhang
    • Visualization
      Xinyuan Yang
    • Writing – original draft
      Xinyuan Yang, Jingyao Zhu, Hantao Xie
    • Conceptualization
      Jianing Zhang
    • Funding acquisition
      Jianing Zhang
    • Project administration
      Jianing Zhang
    • Supervision
      Jianing Zhang
    • Writing – review & editing
      Jianing Zhang