摘要：The Bitcoin (BTC) market presents itself as a new unique medium currency, and it is oftenhailed as the “currency of the future”. Simulating the BTC market in the price discovery processpresents a unique set of market mechanics. The supply of BTC is determined by the number of minersand available BTC and by scripting algorithms for blockchain hashing, while both speculators andinvestors determine demand. One major question then is to understand how BTC is valued and howdifferent factors influence it. In this paper, the BTC market mechanics are broken down using vectorautoregression (VAR) and Bayesian vector autoregression (BVAR) prediction models. The modelsproved 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 contributesto 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 significantprice drivers, including stakeholders impacted, effects of time, as well as supply, demand, and othercharacteristics. The two VAR and BVAR models are compared with some state-of-the-art forecastingmodels over two time periods. Experimental results show that the vector-autoregression-basedmodels achieved better performance compared to the traditional autoregression models and theBayesian regression models.
