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Dynamic spillover and systemic importance analysis of global clean energy companies: A tail risk network perspective

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  • Deng, Jing
  • Zheng, Huike
  • Xing, Xiaoyun

Abstract

This paper conducts tail risk spillover and systemic importance analysis based on the firm-level data of global clean energy system. The tail risk network is constructed based on the ΔCoVaR method, where the correlation is calculated from three perspectives: market, sector and institution. Moreover, the systematically important institutions are identified through a PageRank-based approach. We find that two industries contribute the largest in the risk transmission process, and most of the identified important institutions come from these industries as well. Also, it is observed that the COVID-19 pandemic and the Russia–Ukraine conflict would render different impacts on the risk contagion.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:finlet:v:55:y:2023:i:pb:s1544612323003628
    DOI: 10.1016/j.frl.2023.103990
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    References listed on IDEAS

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    Cited by:

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    3. Gong, Xu & Liao, Qin, 2024. "Physical climate risk attention and dynamic volatility connectedness among new energy stocks," Energy Economics, Elsevier, vol. 136(C).
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    5. Syuhada, Khreshna & Hakim, Arief, 2024. "Risk quantification and validation for green energy markets: New insight from a credibility theory approach," Finance Research Letters, Elsevier, vol. 62(PA).
    6. Jin, Xiu & Liu, Yueli & Yu, Jinming & Chen, Na, 2024. "Extreme risk spillovers in international energy markets: New insights from multilayer networks in the frequency domain," Energy Economics, Elsevier, vol. 139(C).
    7. Wang, Wenyi & Liu, Haifei & Wang, Lei & Che, Zhen, 2025. "Networked liquidity risk contagion in high-carbon sectors: The role of multi-agent behavioral constraints," International Review of Financial Analysis, Elsevier, vol. 106(C).
    8. Liu, Jinglin & Xing, Xiaoyun & Chen, Guorong & Zhang, Yang, 2026. "Biodiversity risk as a financial threat: Evidence from AFHF sectors using QVAR networks," Finance Research Letters, Elsevier, vol. 87(C).
    9. 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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    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
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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