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

Computational Economics volume 57, pages 267–280 (2021)Cite this article

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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Fig. 1

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.

References

  • Anders, N. (2014). The troublesome history of the Bitcoin exchange mtgox. https://anders.io/the-troublesome-history-of-the-bitcoin-exchange-mtgox/

  • Baur, D. G., Dimpfl, T., & Kuck, K. (2018). Bitcoin, gold and the US dollar—A replication and extension. Finance Research Letters, 25, 103–110.

    Article  Google Scholar 

  • Bouri, E., Molnár, P., Azzi, G., Roubaud, D., & Hagfors, L. I. (2017). On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier? Finance Research Letters, 20, 192–198.

    Article  Google Scholar 

  • Brière, M., Oosterlinck, K., & Szafarz, A. (2015). Virtual currency tangible return: Portfolio divesification with Bitcoin. Journal of Asset Management, 16(6), 365–373.

    Article  Google Scholar 

  • Chiang, H. S., Sangaiah, A. K., Chen, M. Y., & Liu, J. Y. (2020). A novel artificial bee colony optimization algorithm with SVM for bio-inspired software-defined networking. International Journal of Parallel Programming, 48, 310–328.

    Article  Google Scholar 

  • Conti, M., Kumar, E. S., Lal, C., & Ruj, S. (2018). A survey on security and privacy issues of bitcoin. IEEE Communications Surveys & Tutorials, 20(4), 3416–3452.

    Article  Google Scholar 

  • Damodaran, A. (2017). The Bitcoin bom asset, currency, commodity or collectible? http://aswathdamodaran.blogspot.com/2017/10/the-bitcoin-boom-asset-currency.html.

  • Daştan, İ., & Gürler, C. (2016). Factors affecting the adoption of mobile payment systems: An empirical analysis. Emerging Markets Journal, 6(1), 17–24.

    Article  Google Scholar 

  • Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar—A GARCH volatility analysis. Finance Research Letters, 16, 85–92.

    Article  Google Scholar 

  • Franklin, M. (2016). A profile of BitCoin currency: An exploratory study. International Journal of Business & Economics Perspectives, 11(1), 80–92.

    Google Scholar 

  • Fujibuchi, W., & Kato, T. (2007). Classification of heterogeneous microarray data by maximum entropy kernel. BMC Bioinformatics, 8(267), 1–10.

    Google Scholar 

  • Grinberg, R. (2012). Bitcoin : An innovative alternative digital currency. Hastings Science and Technology Law Journal, 4, 159–208.

    Google Scholar 

  • Harish, N., Mandal, S., Rao, S., & Patil, S. G. (2015). Particle swarm optimization based support vector machine for damage level prediction of non-reshaped berm breakwater. Applied Soft Computing, 27, 313–321.

    Article  Google Scholar 

  • Harper, J. (2013). What is the value of Bitcoin. CATO At Liberty, https://www.cato.org/blog/what-value-bitcoin.

  • Hsu, C.W., Chang, C.C., & Lin, C.J. (2010). A practical guide to support vector classification, National Taiwan University. https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

  • Ju, L., Lu, T., & Tu, Z. (2016). Capital flight and Bitcoin regulation. International Review of Finance, 16(3), 445–455.

    Article  Google Scholar 

  • Nakamoto, S. (2009). Bitcoin: a peer-to-peer electronic cash system. https://bitcoin.org/bitcoin.pdf.

  • Plassaras, N. A. (2013). Regulating digital currencies: Bringing Bitcoin within the reach of the IMF. Chicago Journal of International Law, 14, 377–407.

    Google Scholar 

  • Quinlan, J. R. (1993). Programs for machine learning. San Fransisco: Morgan Kaufmann.

    Google Scholar 

  • Sebtosheikh, M. A., & Salehi, A. (2015). Lithology prediction by support vector classifiers using inverted seismic Attributes data and petrophysical Logs as a new approach and Investigation of training data set size effect on its performance in a heterogeneous carbonate reservoir. Journal of Petroleum Science and Engineering, 134, 143–149.

    Article  Google Scholar 

  • Takaishi, T., & Adachi, T. (2018). Taylor effect in Bitcoin time series. Economics Letters, 172, 5–7.

    Article  Google Scholar 

  • Tsukerman, M. (2015). The block is hot a survey of the state of Bitcoin regulation and suggestions for the future. Berkeley Technology Law Journal, 30(385), 1127–1170.

    Google Scholar 

  • Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80–82.

    Article  Google Scholar 

  • Wu, C. Y., & Pandey, V. K. (2014). The value of Bitcoin in enhancing the efficiency of an investor’s portfolio. Journal of Financial Planning, 27(9), 44–52.

    Google Scholar 

  • Zhu, Y., Dickinson, D., & Li, J. (2017). Analysis on the influence factors of Bitcoin’s price based on VEC model. Financial Innovation, 3(1), 1–13.

    Article  Google Scholar 

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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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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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  • DOI: https://doi.org/10.1007/s10614-020-10057-7

Keywords

  • Generalized Autoregressive Conditional Heteroskedastic Model (GARCH)
  • Decision tree
  • Support vector machine
  • Bitcoin price