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

被引：13

作者：

Chen, Ting-Hsuan ^{[1
]}

Chen, Mu-Yen ^{[2
]}

Du, Guan-Ting ^{[1
]}

机构：

[1] Natl Taichung Univ Sci & Technol, Taichung, Taiwan

[2] Natl Cheng Kung Univ, Dept Engn Sci, Tainan, Taiwan

来源：

COMPUTATIONAL ECONOMICS | 2021年 / 57卷 / 01期

关键词：

Generalized Autoregressive Conditional Heteroskedastic Model (GARCH); Decision tree; Support vector machine; Bitcoin price; PREDICTION; DOLLAR; GOLD;

D O I：

10.1007/s10614-020-10057-7

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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.

引用

页码：267 / 280

页数：14

共 25 条

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