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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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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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.

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