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The Determinants of Bitcoin’s Price: Utilization of GARCH and Machine Learning Approaches

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  • Ting-Hsuan Chen

    (National Taichung University of Science and Technology)

  • Mu-Yen Chen

    (National Cheng Kung University)

  • Guan-Ting Du

    (National Taichung University of Science and Technology)

Abstract

This study explores the determinants of Bitcoin’s price from 2010 to 2018. This study applies Generalized Autoregressive Conditional Heteroskedastic model to investigate the Bitcoin datasets. The experimental results find the Bitcoin price has positive relationship to the exchange rates (USD/Euro, USD/GBP, USD/CHF and Euro/GBP), the DAX and the Nikkei 225, while a negative relationship with the Fed funds rate, the FTSE 100, and the USD index. Especially, Bitcoin price is significantly affected by the Fed funds rate, followed by the Euro/GBP rate, the USD/GBP rate and the West Texas Intermediate price. This study also executes the decision tree and support vector machine techniques to predict the trend of Bitcoin price. The machine learning approach could be a more suitable methodology than traditional statistics for predicting the Bitcoin price.

Suggested Citation

  • Ting-Hsuan Chen & Mu-Yen Chen & Guan-Ting Du, 2021. "The Determinants of Bitcoin’s Price: Utilization of GARCH and Machine Learning Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 267-280, January.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:1:d:10.1007_s10614-020-10057-7
    DOI: 10.1007/s10614-020-10057-7
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    References listed on IDEAS

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    1. Yechen Zhu & David Dickinson & Jianjun Li, 2017. "Erratum to: Analysis on the influence factors of Bitcoin’s price based on VEC model," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-1, December.
    2. Marie Briere & Kim Oosterlinck & Ariane Szafarz, 2015. "Virtual Currency, Tangible Return: Portfolio Diversification with Bitcoins," Post-Print CEB, ULB -- Universite Libre de Bruxelles, vol. 16(6), pages 365-373.
    3. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    4. Lan Ju & Timothy (Jun) Lu & Zhiyong Tu, 2016. "Capital Flight and Bitcoin Regulation," International Review of Finance, International Review of Finance Ltd., vol. 16(3), pages 445-455, September.
    5. Bouri, Elie & Molnár, Peter & Azzi, Georges & Roubaud, David & Hagfors, Lars Ivar, 2017. "On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?," Finance Research Letters, Elsevier, vol. 20(C), pages 192-198.
    6. Takaishi, Tetsuya & Adachi, Takanori, 2018. "Taylor effect in Bitcoin time series," Economics Letters, Elsevier, vol. 172(C), pages 5-7.
    7. Baur, Dirk G. & Dimpfl, Thomas & Kuck, Konstantin, 2018. "Bitcoin, gold and the US dollar – A replication and extension," Finance Research Letters, Elsevier, vol. 25(C), pages 103-110.
    8. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
    9. Yechen Zhu & David Dickinson & Jianjun Li, 2017. "Analysis on the influence factors of Bitcoin’s price based on VEC model," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-13, December.
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    Cited by:

    1. Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Forecasting, MDPI, vol. 3(2), pages 1-44, May.
    2. Xiaolong Tang & Yuping Song & Xingrui Jiao & Yankun Sun, 2024. "On Forecasting Realized Volatility for Bitcoin Based on Deep Learning PSO–GRU Model," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 2011-2033, May.
    3. Eunho Koo & Geonwoo Kim, 2023. "A New Neural Network Approach for Predicting the Volatility of Stock Market," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1665-1679, April.
    4. Ren, Yi-Shuai & Ma, Chao-Qun & Kong, Xiao-Lin & Baltas, Konstantinos & Zureigat, Qasim, 2022. "Past, present, and future of the application of machine learning in cryptocurrency research," Research in International Business and Finance, Elsevier, vol. 63(C).
    5. Nezir Köse & Hakan Yildirim & Emre Ünal & Boqiang Lin, 2024. "The Bitcoin price and Bitcoin price uncertainty: Evidence of Bitcoin price volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(4), pages 673-695, April.
    6. Gyana Ranjan Patra & Mihir Narayan Mohanty, 2023. "Price Prediction of Cryptocurrency Using a Multi-Layer Gated Recurrent Unit Network with Multi Features," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1525-1544, December.
    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.

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