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What drives the Bitcoin price? A factor augmented error correction mechanism investigation

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  • Łukasz Goczek
  • Ivan Skliarov

Abstract

This article aims to determine what drives the price of Bitcoin. To achieve this aim, a large set of data is analysed using VEC models augmented by factors representing unobservable economic forces. They have been obtained by means of principal component analysis. This method enables us to contribute to the existing literature on Bitcoin in two ways. First, we employ the dimension reduction technique to combine variables from several papers. Second, we estimate several unobservable economic concepts instead of utilizing proxy variables as is usually done. We find that the main factor driving the Bitcoin price is its popularity. Hence, our result not only confirms some previous findings but reinforces them by providing a better definition of popularity. Finally, we conclude that the Bitcoin price is not affected by supply and demand factors in the way that is natural for conventional currencies.

Suggested Citation

  • Łukasz Goczek & Ivan Skliarov, 2019. "What drives the Bitcoin price? A factor augmented error correction mechanism investigation," Applied Economics, Taylor & Francis Journals, vol. 51(59), pages 6393-6410, December.
    • Handle: RePEc:taf:applec:v:51:y:2019:i:59:p:6393-6410
    DOI: 10.1080/00036846.2019.1619021
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    Cited by:

    1. Theodore Panagiotidis & Thanasis Stengos & Orestis Vravosinos, 2020. "A Principal Component-Guided Sparse Regression Approach for the Determination of Bitcoin Returns," JRFM, MDPI, vol. 13(2), pages 1-10, February.
    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. Panagiotidis, Theodore & Papapanagiotou, Georgios & Stengos, Thanasis, 2024. "A Bayesian approach for the determinants of bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 91(C).
    4. Yae, James & Tian, George Zhe, 2022. "Out-of-sample forecasting of cryptocurrency returns: A comprehensive comparison of predictors and algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    5. Qiao, Xingzhi & Zhu, Huiming & Hau, Liya, 2020. "Time-frequency co-movement of cryptocurrency return and volatility: Evidence from wavelet coherence analysis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    6. John E. Marthinsen & Steven R. Gordon, 2022. "The Price and Cost of Bitcoin," Papers 2204.13102, arXiv.org.
    7. Lei Wang & Provash Kumer Sarker & Elie Bouri, 2023. "Short- and Long-Term Interactions Between Bitcoin and Economic Variables: Evidence from the US," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1305-1330, April.
    8. Murat Akkaya, 2021. "The Determinants of the Volatility in Cryptocurrency Markets: The Bitcoin Case," Bogazici Journal, Review of Social, Economic and Administrative Studies, Bogazici University, Department of Economics, vol. 35(1), pages 87-97.
    9. Misha Perepelitsa, 2022. "Elementary Bitcoin economics: from production and transaction demand to values," Papers 2211.07035, arXiv.org.
    10. Zdravka Aljinović & Branka Marasović & Tea Šestanović, 2021. "Cryptocurrency Portfolio Selection—A Multicriteria Approach," Mathematics, MDPI, vol. 9(14), pages 1-21, July.
    11. Alexander, Carol & Deng, Jun & Feng, Jianfen & Wan, Huning, 2023. "Net buying pressure and the information in bitcoin option trades," Journal of Financial Markets, Elsevier, vol. 63(C).
    12. Jong-Min Kim & Chanho Cho & Chulhee Jun, 2022. "Forecasting the Price of the Cryptocurrency Using Linear and Nonlinear Error Correction Model," JRFM, MDPI, vol. 15(2), pages 1-10, February.
    13. Ozkan Haykir & Ibrahim Yagli, 2022. "Speculative bubbles and herding in cryptocurrencies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-33, December.
    14. Burcu Kapar & Jose Olmo, 2021. "Analysis of Bitcoin prices using market and sentiment variables," The World Economy, Wiley Blackwell, vol. 44(1), pages 45-63, January.
    15. Christophe Schinckus & Canh Phuc Nguyen & Felicia Hui Ling Chong, 2023. "Between financial and algorithmic dynamics of cryptocurrencies: An exploratory study," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(3), pages 3055-3070, July.
    16. Gaies, Brahim & Nakhli, Mohamed Sahbi & Sahut, Jean-Michel & Schweizer, Denis, 2023. "Interactions between investors’ fear and greed sentiment and Bitcoin prices," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    17. Aljinović Zdravka & Marasović Branka & Milićević Tea Kalinić, 2022. "The Risk and Return of Traditional and Alternative Investments Under the Impact of COVID-19," Business Systems Research, Sciendo, vol. 13(3), pages 8-22, October.
    18. Carol Alexander & Jun Deng & Jianfen Feng & Huning Wan, 2021. "Net Buying Pressure and the Information in Bitcoin Option Trades," Papers 2109.02776, arXiv.org, revised Mar 2022.
    19. 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).
    20. Pho, Kim Hung & Ly, Sel & Lu, Richard & Hoang, Thi Hong Van & Wong, Wing-Keung, 2021. "Is Bitcoin a better portfolio diversifier than gold? A copula and sectoral analysis for China," International Review of Financial Analysis, Elsevier, vol. 74(C).
    21. Joseph J. French, 2021. "#Bitcoin, #COVID-19: Twitter-Based Uncertainty and Bitcoin Before and during the Pandemic," IJFS, MDPI, vol. 9(2), pages 1-7, May.
    22. Ahmed, Walid M.A., 2022. "Robust drivers of Bitcoin price movements: An extreme bounds analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    23. Palazzi, Rafael Baptista & Júnior, Gerson de Souza Raimundo & Klotzle, Marcelo Cabus, 2021. "The dynamic relationship between bitcoin and the foreign exchange market: A nonlinear approach to test causality between bitcoin and currencies," Finance Research Letters, Elsevier, vol. 42(C).

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