Exploring the determinants of Bitcoin's price: an application of\n Bayesian Structural Time Series

Abstract

Currently, there is no consensus on the real properties of Bitcoin. The\ndiscussion comprises its use as a speculative or safe haven assets, while other\nauthors argue that the augmented attractiveness could end accomplishing money's\nfunctions that economic theory demands. This paper explores the association\nbetween Bitcoin's market price and a set of internal and external factors using\nBayesian Structural Time Series Approach. I aim to contribute to the discussion\nby differentiating among several attractiveness sources and employing a method\nthat provides a more flexible analytic framework that decompose each of the\ncomponents of the time series, apply variable selection, include information on\nprevious studies, and dynamically examine the behavior of the explanatory\nvariables, all in a transparent and tractable setting. The results show that\nthe Bitcoin price is negatively associated with a neutral investor's sentiment,\ngold's price and Yuan to USD exchange rate, while positively related to stock\nmarket index, USD to Euro exchange rate and variated signs among the different\ncountries' search trends. Hence, I find that Bitcoin has mixed properties since\nstill seems to act as a speculative, safe haven and a potential a capital\nflights instrument.\n