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Extreme risk transmission among bitcoin and crude oil markets

Author

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  • Li, Dongxin
  • Hong, Yanran
  • Wang, Lu
  • Xu, Pengfei
  • Pan, Zhigang

Abstract

In the period of extreme events, this paper aims to study the extreme risk transmission between Bitcoin and crude oil market by using the extreme Granger causality test to test their causal relationship under extreme and non-extreme shocks. First, we can obtain different shocks of Bitcoin and crude oil returns based on empirical quantiles. Second, considering the different role that these shocks played in the causality between Bitcoin and crude oil, we conduct our research by testing the causality among different pairwise shocks. Further, given that these relationships may be changed at different time horizons, we also detect them from a frequency-domain perspective. Hence, we not only find the strong evidence of extreme risk transmission between Bitcoin and crude oil but also investigate the time-varying characteristic of this transmission, which may have a great impact on market participants and scholars related to Bitcoin-oil relations.

Suggested Citation

  • Li, Dongxin & Hong, Yanran & Wang, Lu & Xu, Pengfei & Pan, Zhigang, 2022. "Extreme risk transmission among bitcoin and crude oil markets," Resources Policy, Elsevier, vol. 77(C).
    • Handle: RePEc:eee:jrpoli:v:77:y:2022:i:c:s0301420722002094
    DOI: 10.1016/j.resourpol.2022.102761
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    3. Sibande, Xolani & Demirer, Riza & Balcilar, Mehmet & Gupta, Rangan, 2023. "On the pricing effects of bitcoin mining in the fossil fuel market: The case of coal," Resources Policy, Elsevier, vol. 85(PB).
    4. Hong, Yanran & Wang, Lu & Ye, Xiaoqing & Zhang, Yaojie, 2022. "Dynamic asymmetric impact of equity market uncertainty on energy markets: A time-varying causality analysis," Renewable Energy, Elsevier, vol. 196(C), pages 535-546.
    5. Youssef El-Khatib & Abdulnasser Hatemi-J, 2023. "On a regime switching illiquid high volatile prediction model for cryptocurrencies," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 51(2), pages 485-498, July.
    6. Goodell, John W. & Ben Jabeur, Sami & Saâdaoui, Foued & Nasir, Muhammad Ali, 2023. "Explainable artificial intelligence modeling to forecast bitcoin prices," International Review of Financial Analysis, Elsevier, vol. 88(C).
    7. Liu, Guangqiang & Zeng, Qing & Lei, Juan, 2022. "Dynamic risks from climate policy uncertainty: A case study for the natural gas market," Resources Policy, Elsevier, vol. 79(C).
    8. Luo, Keyu & Ye, Yong, 2024. "How responsive are retail electricity prices to crude oil fluctuations in the US? Time-varying and asymmetric perspectives," Research in International Business and Finance, Elsevier, vol. 69(C).
    9. Ben Nouir, Jihed & Ben Haj Hamida, Hayet, 2023. "How do economic policy uncertainty and geopolitical risk drive Bitcoin volatility?," Research in International Business and Finance, Elsevier, vol. 64(C).
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    13. 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).
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    More about this item

    Keywords

    Bitcoin; Crude oil markets; Granger causality; Extreme risk transmission; Time-frequency domain;
    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
    • F39 - International Economics - - International Finance - - - Other
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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