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

Ting-Hsuan Chen, Mu-Yen Chen () and Guan-Ting Du
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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

Computational Economics, 2021, vol. 57, issue 1, No 12, 267-280

Abstract: 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.

Keywords: Generalized Autoregressive Conditional Heteroskedastic Model (GARCH); Decision tree; Support vector machine; Bitcoin price (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (9)

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DOI: 10.1007/s10614-020-10057-7

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