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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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    1. Geng, Jiang-Bo & Du, Ya-Juan & Ji, Qiang & Zhang, Dayong, 2021. "Modeling return and volatility spillover networks of global new energy companies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    2. Nguyen, Cuong C. & Bhatti, M. Ishaq, 2012. "Copula model dependency between oil prices and stock markets: Evidence from China and Vietnam," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(4), pages 758-773.
    3. Dai, Zhifeng & Zhu, Haoyang & Zhang, Xinhua, 2022. "Dynamic spillover effects and portfolio strategies between crude oil, gold and Chinese stock markets related to new energy vehicle," Energy Economics, Elsevier, vol. 109(C).
    4. Cao, Jie & Wen, Fenghua & Stanley, H. Eugene & Wang, Xiong, 2021. "Multilayer financial networks and systemic importance: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 78(C).
    5. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    6. Chen, Yanhua & Pantelous, Athanasios A., 2022. "The U.S.-China trade conflict impacts on the Chinese and U.S. stock markets: A network-based approach," Finance Research Letters, Elsevier, vol. 46(PB).
    7. Sun, Qingru & Gao, Xiangyun & An, Haizhong & Guo, Sui & Liu, Xueyong & Wang, Ze, 2021. "Which time-frequency domain dominates spillover in the Chinese energy stock market?," International Review of Financial Analysis, Elsevier, vol. 73(C).
    8. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    9. Cody Yu-Ling Hsiao & James Morley, 2022. "Debt and financial market contagion," Empirical Economics, Springer, vol. 62(4), pages 1599-1648, April.
    10. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    11. Fry-McKibbin, Renée & Greenwood-Nimmo, Matthew & Hsiao, Cody Yu-Ling & Qi, Lin, 2022. "Higher-order comoment contagion among G20 equity markets during the COVID-19 pandemic," Finance Research Letters, Elsevier, vol. 45(C).
    12. Ouyang, Ruolan & Zhuang, Chengkai & Wang, Tingting & Zhang, Xuan, 2022. "Network analysis of risk transmission among energy futures: An industrial chain perspective," Energy Economics, Elsevier, vol. 107(C).
    13. Yousaf, Imran & Patel, Ritesh & Yarovaya, Larisa, 2022. "The reaction of G20+ stock markets to the Russia–Ukraine conflict “black-swan” event: Evidence from event study approach," Journal of Behavioral and Experimental Finance, Elsevier, vol. 35(C).
    14. Martin Nerlinger & Sebastian Utz, 2022. "The impact of the Russia-Ukraine conflict on the green energy transition – A capital market perspective," Swiss Finance Institute Research Paper Series 22-49, Swiss Finance Institute.
    15. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    16. Wang, Ze & Gao, Xiangyun & An, Haizhong & Tang, Renwu & Sun, Qingru, 2020. "Identifying influential energy stocks based on spillover network," International Review of Financial Analysis, Elsevier, vol. 68(C).
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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. Hsiao, Cody Yu-Ling & Chiu, Yi-Bin, 2024. "Financial contagion and networks among the oil and BRICS stock markets during seven episodes of crisis events," Journal of International Money and Finance, Elsevier, vol. 144(C).
    3. 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).
    4. Domenico Depalo, 2024. "Gloomy expectations after the invasion of Ukraine," Empirical Economics, Springer, vol. 67(1), pages 97-109, July.
    5. 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).
    6. 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).
    7. 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).
    8. 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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