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Article

Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables

1
Computer Science Department, Wilfried Laurier University, Waterloo, ON N2L 3C5, Canada
2
Electrical, Computer, and Biomedical Engineering Department, Ryerson University, Toronto, ON M5B 2K3, Canada
3
IVEY Business School, Management Science Department, London, ON N6G 0N1, Canada
4
Advanced Analytics and Research Lab, Toronto, ON M5J 2P1, Canada
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2020, 13(9), 189; https://doi.org/10.3390/jrfm13090189
Received: 29 July 2020 / Revised: 15 August 2020 / Accepted: 17 August 2020 / Published: 19 August 2020
(This article belongs to the Collection Blockchain and Cryptocurrencies)
The Bitcoin (BTC) market presents itself as a new unique medium currency, and it is often hailed as the “currency of the future”. Simulating the BTC market in the price discovery process presents a unique set of market mechanics. The supply of BTC is determined by the number of miners and available BTC and by scripting algorithms for blockchain hashing, while both speculators and investors determine demand. One major question then is to understand how BTC is valued and how different factors influence it. In this paper, the BTC market mechanics are broken down using vector autoregression (VAR) and Bayesian vector autoregression (BVAR) prediction models. The models proved to be very useful in simulating past BTC prices using a feature set of exogenous variables. The VAR model allows the analysis of individual factors of influence. This analysis contributes to an in-depth understanding of what drives BTC, and it can be useful to numerous stakeholders. This paper’s primary motivation is to capitalize on market movement and identify the significant price drivers, including stakeholders impacted, effects of time, as well as supply, demand, and other characteristics. The two VAR and BVAR models are compared with some state-of-the-art forecasting models over two time periods. Experimental results show that the vector-autoregression-based models achieved better performance compared to the traditional autoregression models and the Bayesian regression models. View Full-Text
Keywords: Bitcoin; blockchain; autoregression; time-series analysis; simulation; predictive modes; endogenous; exogenous variables Bitcoin; blockchain; autoregression; time-series analysis; simulation; predictive modes; endogenous; exogenous variables
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MDPI and ACS Style

Ibrahim, A.; Kashef, R.; Li, M.; Valencia, E.; Huang, E. Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables. J. Risk Financial Manag. 2020, 13, 189. https://doi.org/10.3390/jrfm13090189

AMA Style

Ibrahim A, Kashef R, Li M, Valencia E, Huang E. Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables. Journal of Risk and Financial Management. 2020; 13(9):189. https://doi.org/10.3390/jrfm13090189

Chicago/Turabian Style

Ibrahim, Ahmed, Rasha Kashef, Menglu Li, Esteban Valencia, and Eric Huang. 2020. "Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables" Journal of Risk and Financial Management 13, no. 9: 189. https://doi.org/10.3390/jrfm13090189

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