IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v82y2023ics0301420723001459.html
   My bibliography  Save this article

Oil price and the Bitcoin market

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301420723001459
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2023.103437?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    10. 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.
    11. 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.
    12. 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).
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Yen, Kuang-Chieh & Cheng, Hui-Pei, 2021. "Economic policy uncertainty and cryptocurrency volatility," Finance Research Letters, Elsevier, vol. 38(C).
    18. 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.
    19. 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.
    20. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Salisu, Afees A. & Isah, Kazeem O. & Raheem, Ibrahim D., 2019. "Testing the predictability of commodity prices in stock returns of G7 countries: Evidence from a new approach," Resources Policy, Elsevier, vol. 64(C).
    2. Salisu, Afees A. & Swaray, Raymond & Oloko, Tirimisiyu F., 2019. "Improving the predictability of the oil–US stock nexus: The role of macroeconomic variables," Economic Modelling, Elsevier, vol. 76(C), pages 153-171.
    3. Salisu, Afees A. & Raheem, Ibrahim D. & Ndako, Umar B., 2019. "A sectoral analysis of asymmetric nexus between oil price and stock returns," International Review of Economics & Finance, Elsevier, vol. 61(C), pages 241-259.
    4. Salisu, Afees A. & Isah, Kazeem O., 2018. "Predicting US inflation: Evidence from a new approach," Economic Modelling, Elsevier, vol. 71(C), pages 134-158.
    5. Afees A. Salisu & Kazeem Isah & Ibrahim D. Raheem, 2018. "Testing the predictability of commodity prices in stock returns: A new perspective," Working Papers 061, Centre for Econometric and Allied Research, University of Ibadan.
    6. Afees A. Salisu & Juncal Cunado & Kazeem Isah & Rangan Gupta, 2020. "Oil Price and Exchange Rate Behaviour of the BRICS for Over a Century," Working Papers 202064, University of Pretoria, Department of Economics.
    7. Salisu, Afees A. & Olaniran, Abeeb & Lasisi, Lukman, 2023. "Climate risk and gold," Resources Policy, Elsevier, vol. 82(C).
    8. Salisu, Afees A. & Ademuyiwa, Idris & Isah, Kazeem O., 2018. "Revisiting the forecasting accuracy of Phillips curve: The role of oil price," Energy Economics, Elsevier, vol. 70(C), pages 334-356.
    9. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2015. "Stock return forecasting: Some new evidence," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 38-51.
    10. Afees A. Salisu & Abdulsalam Abidemi Sikiru, 2021. "Palm Oil Price–Exchange Rate Nexus In Indonesia And Malaysia," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 24(2), pages 169-180, June.
    11. Ma, Feng & Liu, Jing & Wahab, M.I.M. & Zhang, Yaojie, 2018. "Forecasting the aggregate oil price volatility in a data-rich environment," Economic Modelling, Elsevier, vol. 72(C), pages 320-332.
    12. Elie Bouri & Afees A. Salisu & Rangan Gupta, 2022. "Bitcoin Prices and the Realized Volatility of US Sectoral Stock Returns," Working Papers 202224, University of Pretoria, Department of Economics.
    13. Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna & Mark E. Wohar, 2022. "Uncertainty and predictability of real housing returns in the United Kingdom: A regional analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1525-1556, November.
    14. Narayan, Paresh Kumar & Phan, Dinh Hoang Bach & Narayan, Seema, 2018. "Technology-investing countries and stock return predictability," Emerging Markets Review, Elsevier, vol. 36(C), pages 159-179.
    15. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Tran, Vuong Thao, 2018. "Can economic policy uncertainty predict stock returns? Global evidence," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 55(C), pages 134-150.
    16. Salisu, Afees A. & Gupta, Rangan & Ji, Qiang, 2022. "Forecasting oil prices over 150 years: The role of tail risks," Resources Policy, Elsevier, vol. 75(C).
    17. Rehman, Mobeen Ur & Raheem, Ibrahim D. & Zeitun, Rami & Vo, Xuan Vinh & Ahmad, Nasir, 2023. "Do oil shocks affect the green bond market?," Energy Economics, Elsevier, vol. 117(C).
    18. Elie Bouri & Afees A. Salisu & Rangan Gupta, 2023. "The predictive power of Bitcoin prices for the realized volatility of US stock sector returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-22, December.
    19. Salisu, Afees A. & Vo, Xuan Vinh, 2021. "The behavior of exchange rate and stock returns in high and low interest rate environments," International Review of Economics & Finance, Elsevier, vol. 74(C), pages 138-149.
    20. Sharma, Susan Sunila & Phan, Dinh Hoang Bach & Iyke, Bernard, 2019. "Do oil prices predict Indonesian macroeconomy?," Economic Modelling, Elsevier, vol. 82(C), pages 2-12.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jrpoli:v:82:y:2023:i:c:s0301420723001459. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.