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The impact of the Russia–Ukraine conflict on the energy subsector stocks in China: A network-based approach

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  • Xing, Xiaoyun
  • Xu, Zihan
  • Chen, Ying
  • Ouyang, WenPei
  • Deng, Jing
  • Pan, Huanxue

Abstract

This paper explores the impact of the Russia–Ukraine conflict on the risk transmission of energy subsector stocks in China. Risk spillovers are quantified by the Diebold and Yilmaz index model. Based on the minimum spanning tree analysis, we identify the systematically important energy stocks and the shortest transmission paths. The results show that the key energy stocks in terms of volatility correlations would certainly change during the Russia–Ukraine conflict. In normal times, the traditional stocks act as the hubs of risk contagion. However, during the conflict, several renewable energy stocks tend to influence other stocks in the system.

Suggested Citation

  • Xing, Xiaoyun & Xu, Zihan & Chen, Ying & Ouyang, WenPei & Deng, Jing & Pan, Huanxue, 2023. "The impact of the Russia–Ukraine conflict on the energy subsector stocks in China: A network-based approach," Finance Research Letters, Elsevier, vol. 53(C).
    • Handle: RePEc:eee:finlet:v:53:y:2023:i:c:s1544612323000193
    DOI: 10.1016/j.frl.2023.103645
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    Cited by:

    1. Deng, Jing & Zheng, Huike & Xing, Xiaoyun, 2023. "Dynamic spillover and systemic importance analysis of global clean energy companies: A tail risk network perspective," Finance Research Letters, Elsevier, vol. 55(PB).
    2. Xing, Xiaoyun & Chen, Ying & Wang, Xiuya & Li, Boyao & Deng, Jing, 2023. "The impact of national carbon market establishment on risk transmission among carbon and energy markets in China: A systemic importance analysis," Finance Research Letters, Elsevier, vol. 57(C).

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    More about this item

    Keywords

    The Russia–Ukraine conflict; Energy stock market; Risk transmission; Network analysis;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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