Exploring the dynamics of Bitcoin’s price: a Bayesian structural time series approach
Obryan Poyser ()
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Obryan Poyser: Universitat Autònoma de Barcelona
Eurasian Economic Review, 2019, vol. 9, issue 1, No 2, 29-60
Abstract Currently, there is no consensus on the real properties of Bitcoin. The discussion comprises its use as a speculative or safe haven asset, while other authors argue that the augmented attractiveness could end up accomplishing money’s properties that economic theory demands. This paper explores the association between Bitcoin’s market price and a set of internal and external factors by employing the Bayesian structural time series approach (BSTS). The idea behind BSTS is to create a superposition of layers such as cycles, trend, and explanatory variables that are allowed to vary stochastically over time, additionally, it is possible to perform a variable selection through the application of the Spike and Slab method. This study aims to contribute to the discussion of Bitcoin price determinants by differentiating among several attractiveness sources and employing a method that provides a more flexible analytic framework that decomposes each of the components of the time series, applies variable selection, includes information on previous studies, and dynamically examines the behavior of the explanatory variables, all in a transparent and tractable setting. The results show that the Bitcoin’s price is negatively associated with the price of gold as well as the exchange rate between Yuan and US Dollar, while positively correlated to stock market index, USD to Euro exchange rate and diverse signs among the different countries’ search trends.
Keywords: Cryptocurrencies; Bayesian time series; Blockchain; Bitcoin; State space (search for similar items in EconPapers)
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