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Oil price and the Bitcoin market

Author

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  • Salisu, Afees A.
  • Ndako, Umar B.
  • Vo, Xuan Vinh

Abstract

Motivated by the significant role oil plays in the production of Bitcoin, we test whether its price can influence the realized volatility of Bitcoin returns. Using data over the period of January 27, 2017 (coinciding with the emergence of Bitcoin bubbles) to June 3, 2022, we conduct some predictability analyses and establish the following outcomes. First, we find that higher oil prices tend to raise the cost of producing Bitcoins, therefore lowering its returns and by extension its trading and volatility. Second, we find improved forecast performance of oil price for the realized volatility of Bitcoin as our proposed model that accounts for oil price consistently outperforms the benchmark (random walk) model, regardless of the oil price variant and forecast horizon. Third, investors in the Bitcoin market that observe oil price movements when making investment decisions are more likely to derive higher economic gains than their counterparts that ignore it.

Suggested Citation

  • Salisu, Afees A. & Ndako, Umar B. & Vo, Xuan Vinh, 2023. "Oil price and the Bitcoin market," Resources Policy, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:jrpoli:v:82:y:2023:i:c:s0301420723001459
    DOI: 10.1016/j.resourpol.2023.103437
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    References listed on IDEAS

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    1. Syed Jawad Hussain Shahzad & Elie Bouri & Naveed Raza & David Roubaud, 2019. "Asymmetric impacts of disaggregated oil price shocks on uncertainties and investor sentiment," Review of Quantitative Finance and Accounting, Springer, vol. 52(3), pages 901-921, April.
    2. Michel Rauchs & Garrick Hileman, 2017. "Global Cryptocurrency Benchmarking Study," Cambridge Centre for Alternative Finance Reports 201704-gcbs, Cambridge Centre for Alternative Finance, Cambridge Judge Business School, University of Cambridge.
    3. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    4. Joakim Westerlund & Paresh Narayan, 2015. "Testing for Predictability in Conditionally Heteroskedastic Stock Returns," Journal of Financial Econometrics, Oxford University Press, vol. 13(2), pages 342-375.
    5. Salisu, Afees A. & Isah, Kazeem O., 2017. "Revisiting the oil price and stock market nexus: A nonlinear Panel ARDL approach," Economic Modelling, Elsevier, vol. 66(C), pages 258-271.
    6. Gkillas, Konstantinos & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2022. "Spillovers in Higher-Order Moments of Crude Oil, Gold, and Bitcoin," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 398-406.
    7. Nuruddeen Usman & Kodili Nwanneka & Nduka, 2023. "Announcement Effect of COVID-19 on Cryptocurrencies," Asian Economics Letters, Asia-Pacific Applied Economics Association, vol. 3(3), pages 1-4.
    8. Max J. Krause & Thabet Tolaymat, 2018. "Author Correction: Quantification of energy and carbon costs for mining cryptocurrencies," Nature Sustainability, Nature, vol. 1(12), pages 814-814, December.
    9. Swaray, Raymond & Salisu, Afees A., 2018. "A firm-level analysis of the upstream-downstream dichotomy in the oil-stock nexus," Global Finance Journal, Elsevier, vol. 37(C), pages 199-218.
    10. Narayan, Paresh Kumar & Gupta, Rangan, 2015. "Has oil price predicted stock returns for over a century?," Energy Economics, Elsevier, vol. 48(C), pages 18-23.
    11. Yen, Kuang-Chieh & Cheng, Hui-Pei, 2021. "Economic policy uncertainty and cryptocurrency volatility," Finance Research Letters, Elsevier, vol. 38(C).
    12. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    13. Westerlund, Joakim & Narayan, Paresh Kumar, 2012. "Does the choice of estimator matter when forecasting returns?," Journal of Banking & Finance, Elsevier, vol. 36(9), pages 2632-2640.
    14. Yin, Libo & Nie, Jing & Han, Liyan, 2021. "Understanding cryptocurrency volatility: The role of oil market shocks," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 233-253.
    15. Jin, Jingyu & Yu, Jiang & Hu, Yang & Shang, Yue, 2019. "Which one is more informative in determining price movements of hedging assets? Evidence from Bitcoin, gold and crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    16. 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).
    17. Max J. Krause & Thabet Tolaymat, 2018. "Quantification of energy and carbon costs for mining cryptocurrencies," Nature Sustainability, Nature, vol. 1(11), pages 711-718, November.
    18. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    19. Rehman, Mobeen Ur & Kang, Sang Hoon, 2021. "A time–frequency comovement and causality relationship between Bitcoin hashrate and energy commodity markets," Global Finance Journal, Elsevier, vol. 49(C).
    20. Salisu, Afees A. & Isah, Kazeem O. & Oyewole, Oluwatomisin J. & Akanni, Lateef O., 2017. "Modelling oil price-inflation nexus: The role of asymmetries," Energy, Elsevier, vol. 125(C), pages 97-106.
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    Cited by:

    1. Qin, Meng & Wu, Tong & Ma, Xuecheng & Albu, Lucian Liviu & Umar, Muhammad, 2023. "Are energy consumption and carbon emission caused by Bitcoin? A novel time-varying technique," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 109-120.
    2. Ghaemi Asl, Mahdi & Raheem, Ibrahim D. & Rashidi, Muhammad Mahdi, 2023. "Do stochastic risks flow between industrial and precious metals, Islamic stocks, green bonds, green stocks, clean investments, major foreign exchange rates, and Bitcoin?," Resources Policy, Elsevier, vol. 86(PA).
    3. Yousaf, Imran & Assaf, Ata & Demir, Ender, 2024. "Relationship between real estate tokens and other asset classes: Evidence from quantile connectedness approach," Research in International Business and Finance, Elsevier, vol. 69(C).
    4. Wu, Xinyu & Yin, Xuebao & Umar, Zaghum & Iqbal, Najaf, 2023. "Volatility forecasting in the Bitcoin market: A new proposed measure based on the VS-ACARR approach," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).

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    More about this item

    Keywords

    Bitcoin market; Oil prices; Realized volatility prediction; Economic gains;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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