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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.

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  • 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. Ferriani, Fabrizio & Gazzani, Andrea, 2023. "The invasion of Ukraine and the energy crisis: Comparative advantages in equity valuations," Finance Research Letters, Elsevier, vol. 58(PD).
    2. Jin, Xiu & Xue, Qiuyang, 2023. "Retail investor attention and stock market behavior in Russia-Ukraine conflict based on Chinese practices: Evidence from transfer entropy causal network," Finance Research Letters, Elsevier, vol. 58(PB).
    3. Balash, Vladimir & Faizliev, Alexey, 2024. "Volatility spillovers across Russian oil and gas sector. Evidence of the impact of global markets and extraordinary events," Energy Economics, Elsevier, vol. 129(C).
    4. Lu, Xunfa & Huang, Nan & Mo, Jianlei, 2024. "Time-varying causalities from the COVID-19 media coverage to the dynamic spillovers among the cryptocurrency, the clean energy, and the crude oil," Energy Economics, Elsevier, vol. 132(C).
    5. Domenico Depalo, 2024. "Gloomy expectations after the invasion of Ukraine," Empirical Economics, Springer, vol. 67(1), pages 97-109, July.
    6. 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).
    7. 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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