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

TL;DR

This paper uses Bayesian Structural Time Series to analyze how various internal and external factors influence Bitcoin's price, revealing its mixed role as a speculative asset, safe haven, and capital flight instrument.

Contribution

It introduces a flexible Bayesian approach to decompose Bitcoin's price components and examine the dynamic influence of multiple factors, enhancing understanding of its complex behavior.

Findings

Bitcoin price negatively associated with investor sentiment, gold, and Yuan to USD exchange rate

Positively related to stock index and USD to Euro exchange rate

Search trends vary across countries, indicating diverse influences

Abstract

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