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

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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.

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Notes

  1. Banks could borrow money for short periods (typically overnight) to make up transitory cash shortfalls. The interest rate that is paid on these borrowed reserves is called the federal funds rate.

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Authors and Affiliations

  1. National Taichung University of Science and Technology, Taichung, Taiwan

    Ting-Hsuan Chen & Guan-Ting Du

  2. Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan

    Mu-Yen Chen

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  1. Ting-Hsuan Chen
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  2. Mu-Yen Chen
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  3. Guan-Ting Du
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Correspondence to Mu-Yen Chen.

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Chen, TH., Chen, MY. & Du, GT. The Determinants of Bitcoin’s Price: Utilization of GARCH and Machine Learning Approaches. Comput Econ 57, 267–280 (2021). https://doi.org/10.1007/s10614-020-10057-7

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