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Spillovers from the Russia-Ukraine conflict

Author

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  • Yang, Yajie
  • Zhao, Longfeng
  • Zhu, Yipin
  • Chen, Lin
  • Wang, Gangjin
  • Wang, Chao

Abstract

We investigate dynamic integration and risk transmission among major global financial markets around the Russia-Ukraine conflict by implementing the TVP-VAR frequency model for both a high and a low-frequency band. We also employ wavelet coherence to supplement our analysis. Results show that (i) the global financial system is highly connected during the entire period, and the dynamic spillovers reach unprecedented heights on the day of the Russia-Ukraine conflict; (ii) Russia comes out as a spillover leader, whereas the geopolitical risk index, Dow Jones Commodity, and Bitcoin are the major targets of spillovers; and (iii) for the entire sample period, short-term connectedness gains much prominence. This study uncovers the real landscape of financial spillover effects of the Russia-Ukraine conflict, which can help investors and policymakers manage the risk exposure and avoid unexpected losses.

Suggested Citation

  • Yang, Yajie & Zhao, Longfeng & Zhu, Yipin & Chen, Lin & Wang, Gangjin & Wang, Chao, 2023. "Spillovers from the Russia-Ukraine conflict," Research in International Business and Finance, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:riibaf:v:66:y:2023:i:c:s0275531923001320
    DOI: 10.1016/j.ribaf.2023.102006
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    Cited by:

    1. Lin, Zi-Luo & Ouyang, Wen-Pei & Yu, Qing-Rui, 2024. "Risk spillover effects of the Israel–Hamas War on global financial and commodity markets: A time–frequency and network analysis," Finance Research Letters, Elsevier, vol. 66(C).
    2. Abdollahi, Hooman & Junttila, Juha-Pekka & Lehkonen, Heikki, 2024. "Clustering asset markets based on volatility connectedness to political news," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 93(C).
    3. Zhou, Xiaoran & Enilov, Martin & Parhi, Mamata, 2024. "Does oil spin the commodity wheel? Quantile connectedness with a common factor error structure across energy and agricultural markets," Energy Economics, Elsevier, vol. 132(C).
    4. Zhang, Yi & Zhou, Long & Wu, Baoxiu & Liu, Fang, 2024. "Tail risk transmission from the United States to emerging stock Markets: Empirical evidence from multivariate quantile analysis," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).
    5. Wang, Bo & Xiao, Yang, 2024. "Measuring spatial impacts and tracking cross-border risk," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 50-84.
    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. Ghaemi Asl, Mahdi & Ben Jabeur, Sami, 2024. "Could the Russia-Ukraine war stir up the persistent memory of interconnectivity among Islamic equity markets, energy commodities, and environmental factors?," Research in International Business and Finance, Elsevier, vol. 69(C).

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    More about this item

    Keywords

    Russia-Ukraine conflict; TVP-VAR; Risk spillover network; Frequency connectedness;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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