[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/103812.html
   My bibliography  Save this paper

Does the hashrate affect the bitcoin price?

Author

Listed:
  • Fantazzini, Dean
  • Kolodin, Nikita
Abstract
This paper investigates the relationship between the bitcoin price and the hashrate by disentangling the effects of the energy efficiency of the bitcoin mining equipment, bitcoin halving, and of structural breaks on the price dynamics. For this purpose, we propose a methodology based on exponential smoothing to model the dynamics of the Bitcoin network energy efficiency. We consider either directly the hashrate or the bitcoin cost-of-production model (CPM) as a proxy for the hashrate, to take any nonlinearity into account. In the first examined sub-sample (01/08/2016-04/12/2017), the hashrate and the CPMs were never significant, while a significant cointegration relationship was found in the second sub-sample (11/12/2017-24/02/2020). The empirical evidence shows that it is better to consider the hashrate directly rather than its proxy represented by the CPM when modeling its relationship with the bitcoin price. Moreover, the causality is always uni-directional going from the bitcoin price to the hashrate (or its proxies), with lags ranging from 1 week up to 6 weeks later. These findings are consistent with a large literature in energy economics, which showed that oil and gas returns affect the purchase of the drilling rigs with a delay of up to 3 months, whereas the impact of changes in the rig count on oil and gas returns is limited or not significant.

Suggested Citation

  • Fantazzini, Dean & Kolodin, Nikita, 2020. "Does the hashrate affect the bitcoin price?," MPRA Paper 103812, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:103812
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/103812/1/MPRA_paper_103812.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Helmut Lütkepohl, 2005. "New Introduction to Multiple Time Series Analysis," Springer Books, Springer, number 978-3-540-27752-1, December.
    2. Sa-ngasoongsong, Akkarapol & Bukkapatnam, Satish T.S. & Kim, Jaebeom & Iyer, Parameshwaran S. & Suresh, R.P., 2012. "Multi-step sales forecasting in automotive industry based on structural relationship identification," International Journal of Production Economics, Elsevier, vol. 140(2), pages 875-887.
    3. Sokbae Lee & Myung Hwan Seo & Youngki Shin, 2017. "Correction," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 883-883, April.
    4. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.
    5. Corbet, Shaen & Lucey, Brian & Yarovaya, Larisa, 2018. "Datestamping the Bitcoin and Ethereum bubbles," Finance Research Letters, Elsevier, vol. 26(C), pages 81-88.
    6. Köchling, Gerrit & Müller, Janis & Posch, Peter N., 2019. "Does the introduction of futures improve the efficiency of Bitcoin?," Finance Research Letters, Elsevier, vol. 30(C), pages 367-370.
    7. Fry, John, 2018. "Booms, busts and heavy-tails: The story of Bitcoin and cryptocurrency markets?," Economics Letters, Elsevier, vol. 171(C), pages 225-229.
    8. Greenslade, Jennifer V. & Hall, Stephen G. & Henry, S. G. Brian, 2002. "On the identification of cointegrated systems in small samples: a modelling strategy with an application to UK wages and prices," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1517-1537, August.
    9. Xiong, Jinwu & Liu, Qing & Zhao, Lei, 2020. "A new method to verify Bitcoin bubbles: Based on the production cost," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    10. Toda, Hiro Y. & Yamamoto, Taku, 1995. "Statistical inference in vector autoregressions with possibly integrated processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 225-250.
    11. Junsoo Lee & Mark C. Strazicich, 2003. "Minimum Lagrange Multiplier Unit Root Test with Two Structural Breaks," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 1082-1089, November.
    12. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    13. Khalifa, Ahmed & Caporin, Massimiliano & Hammoudeh, Shawkat, 2017. "The relationship between oil prices and rig counts: The importance of lags," Energy Economics, Elsevier, vol. 63(C), pages 213-226.
    14. Adam Hayes, 2018. "Bitcoin price and its marginal cost of production: support for a fundamental value," Papers 1805.07610, arXiv.org.
    15. Jan-Christian Gerlach & Guilherme Demos & Didier Sornette, 2018. "Dissection of Bitcoin's Multiscale Bubble History from January 2012 to February 2018," Papers 1804.06261, arXiv.org, revised May 2019.
    16. Hattori, Takahiro & Ishida, Ryo, 2021. "Did the introduction of Bitcoin futures crash the Bitcoin market at the end of 2017?," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    17. Vogelsang, Timothy J & Perron, Pierre, 1998. "Additional Tests for a Unit Root Allowing for a Break in the Trend Function at an Unknown Time," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 1073-1100, November.
    18. G. J. Santoni, 1987. "The great bull markets 1924-29 and 1982-87: speculative bubbles or economic fundamentals?," Review, Federal Reserve Bank of St. Louis, issue Nov, pages 16-30.
    19. Narasimhan Jegadeesh & Sheridan Titman, 2001. "Profitability of Momentum Strategies: An Evaluation of Alternative Explanations," Journal of Finance, American Finance Association, vol. 56(2), pages 699-720, April.
    20. Esmail Ansari & Robert. K. Kaufmann, 2019. "The effect of oil and gas price and price volatility on rig activity in tight formations and OPEC strategy," Nature Energy, Nature, vol. 4(4), pages 321-328, April.
    21. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    22. Adam Hayes, 2015. "A Cost of Production Model for Bitcoin," Working Papers 1505, New School for Social Research, Department of Economics.
    23. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    24. Adam S. Hayes, 2019. "Bitcoin price and its marginal cost of production: support for a fundamental value," Applied Economics Letters, Taylor & Francis Journals, vol. 26(7), pages 554-560, April.
    25. Frode Kjærland & Aras Khazal & Erlend A. Krogstad & Frans B. G. Nordstrøm & Are Oust, 2018. "An Analysis of Bitcoin’s Price Dynamics," JRFM, MDPI, vol. 11(4), pages 1-18, October.
    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. Dean Fantazzini, 2022. "Crypto-Coins and Credit Risk: Modelling and Forecasting Their Probability of Death," JRFM, MDPI, vol. 15(7), pages 1-34, July.
    2. Marthinsen, John E. & Gordon, Steven R., 2022. "The price and cost of bitcoin," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 280-288.
    3. Juan C. King & Roberto Dale & Jos'e M. Amig'o, 2024. "Blockchain Metrics and Indicators in Cryptocurrency Trading," Papers 2403.00770, arXiv.org.
    4. Pardis Roozkhosh & Alireza Pooya, 2024. "Dynamic Analysis of Bitcoin Price Under Market News and Sentiments and Government Support Policies," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1163-1198, August.
    5. Dean Fantazzini & Raffaella Calabrese, 2021. "Crypto Exchanges and Credit Risk: Modeling and Forecasting the Probability of Closure," JRFM, MDPI, vol. 14(11), pages 1-23, October.
    6. Mingbo Zheng & Gen-Fu Feng & Xinxin Zhao & Chun-Ping Chang, 2023. "The transaction behavior of cryptocurrency and electricity consumption," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-18, December.
    7. Carbó, José Manuel & Gorjón, Sergio, 2024. "Determinants of the price of bitcoin: An analysis with machine learning and interpretability techniques," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 123-140.
    8. John E. Marthinsen & Steven R. Gordon, 2022. "The Price and Cost of Bitcoin," Papers 2204.13102, arXiv.org.
    9. Clément Landormy, 2024. "An inquiry of Bitcoin price formation: Evidence from Linear and Nonlinear ARDL Frameworks, 2017-2018," Working Papers of BETA 2024-31, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    10. David Cerezo Sánchez, 2022. "Pravuil: Global Consensus for a United World," FinTech, MDPI, vol. 1(4), pages 1-20, October.
    11. Julien Chevallier & Bilel Sanhaji, 2023. "Jump-Robust Realized-GARCH-MIDAS-X Estimators for Bitcoin and Ethereum Volatility Indices," Stats, MDPI, vol. 6(4), pages 1-32, December.
    12. King, Juan C. & Dale, Roberto & Amigó, José M., 2024. "Blockchain metrics and indicators in cryptocurrency trading," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    13. David Cerezo S'anchez, 2021. "Pravuil: Global Consensus for a United World," Papers 2105.10464, arXiv.org.
    14. Juraj Fabus & Iveta Kremenova & Natalia Stalmasekova & Terezia Kvasnicova-Galovicova, 2024. "An Empirical Examination of Bitcoin’s Halving Effects: Assessing Cryptocurrency Sustainability within the Landscape of Financial Technologies," JRFM, MDPI, vol. 17(6), pages 1-23, May.
    15. Yang, Zixiu & Fantazzini, Dean, 2022. "Using crypto assets pricing methods to build technical oscillators for short-term bitcoin trading," MPRA Paper 115508, University Library of Munich, Germany.
    16. Kubal, Jan & Kristoufek, Ladislav, 2022. "Exploring the relationship between Bitcoin price and network’s hashrate within endogenous system," International Review of Financial Analysis, Elsevier, vol. 84(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. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.
    2. Frode Kjærland & Aras Khazal & Erlend A. Krogstad & Frans B. G. Nordstrøm & Are Oust, 2018. "An Analysis of Bitcoin’s Price Dynamics," JRFM, MDPI, vol. 11(4), pages 1-18, October.
    3. Dean Fantazzini, 2014. "Nowcasting and Forecasting the Monthly Food Stamps Data in the US Using Online Search Data," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-27, November.
    4. Dean Fantazzini, 2022. "Crypto-Coins and Credit Risk: Modelling and Forecasting Their Probability of Death," JRFM, MDPI, vol. 15(7), pages 1-34, July.
    5. John D. Levendis, 2018. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-98282-3, June.
    6. Perles-Ribes, José Francisco & Ramón-Rodríguez, Ana Belén & Rubia, Antonio & Moreno-Izquierdo, Luis, 2017. "Is the tourism-led growth hypothesis valid after the global economic and financial crisis? The case of Spain 1957–2014," Tourism Management, Elsevier, vol. 61(C), pages 96-109.
    7. Assaf, Ata & Demir, Ender & Ersan, Oguz, 2024. "Detecting and date-stamping bubbles in fan tokens," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 98-113.
    8. Helmut Lütkepohl, 2013. "Vector autoregressive models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 6, pages 139-164, Edward Elgar Publishing.
    9. Oyeyinka OMOSHORO-JONES, 2020. "Investigating The Government Revenue–Expenditure Nexus: Empirical Evidence For The Free State Province In A Multivariate Model," Theoretical and Practical Research in the Economic Fields, ASERS Publishing, vol. 11(2), pages 138-156.
    10. Süssmuth, Bernd, 2019. "Bitcoin and Web Search Query Dynamics: Is the price driving the hype or is the hype driving the price?," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203566, Verein für Socialpolitik / German Economic Association.
    11. Lusine Lusinyan & John Thornton, 2011. "Unit roots, structural breaks and cointegration in the UK public finances, 1750-2004," Applied Economics, Taylor & Francis Journals, vol. 43(20), pages 2583-2592.
    12. Pan, Lei & Mishra, Vinod, 2018. "Stock market development and economic growth: Empirical evidence from China," Economic Modelling, Elsevier, vol. 68(C), pages 661-673.
    13. David Greasley & Les Oxley, 2010. "Cliometrics And Time Series Econometrics: Some Theory And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 24(5), pages 970-1042, December.
    14. Arun Narayanasamy & Humnath Panta & Rohit Agarwal, 2023. "Relations among Bitcoin Futures, Bitcoin Spot, Investor Attention, and Sentiment," JRFM, MDPI, vol. 16(11), pages 1-24, November.
    15. Haffar, Adlane & Le Fur, Eric, 2021. "Structural vector error correction modelling of Bitcoin price," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 170-178.
    16. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    17. Yang, Zixiu & Fantazzini, Dean, 2022. "Using crypto assets pricing methods to build technical oscillators for short-term bitcoin trading," MPRA Paper 115508, University Library of Munich, Germany.
    18. B. Faye & E. Le Fur & S. Prat, 2015. "Dynamics of fine wine and asset prices: evidence from short- and long-run co-movements," Applied Economics, Taylor & Francis Journals, vol. 47(29), pages 3059-3077, June.
    19. Özgür Özaydın* & H. Alper Güzel, 2019. "Oil Consumption and Economic Growth in Turkey: An ARDL Bounds Test Approach in the Presence of Structural Breaks," Business, Management and Economics Research, Academic Research Publishing Group, vol. 5(6), pages 77-85, 06-2019.
    20. Tiwari, Aviral Kumar, 2012. "An empirical investigation of causality between producers' price and consumers' price indices in Australia in frequency domain," Economic Modelling, Elsevier, vol. 29(5), pages 1571-1578.

    More about this item

    Keywords

    Bitcoin; energy e�ciency; mining; hashrate; bitcoin price;
    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
    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E41 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Demand for Money
    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:pra:mprapa:103812. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.