Exploring the Determinants of Bitcoin Exchange Rate

By Xin Li, C. Wang. Social Science Research Network, 2014

418 citations recorded by the source corpus.

Bitcoin, a digital currency created based on modern P2P and cryptograph technologies, has ignited much discussion among professionals. However, there is a lack of empirical understanding about the Bitcoin exchange rate. In this paper, we propose a theoretical framework from an economic-technological perspective for understanding determination of the Bitcoin exchange rate and to empirically examine dynamics of the Bitcoin exchange rate against the USD in an error correction model. Our findings suggest that it is critical to extend traditional economic exchange rate models and treat Bitcoin as both a medium for economic transactions and a technology innovation. Specifically, we find that the Bitcoin exchange rate responds to short-term changes in the US inflation rate and money supply as well as the market size of the Bitcoin economy as measured by the total number of transactions and total Bitcoin value of the transactions. Meanwhile, public recognition, reflected by Google search index and number of Twitter mentions, as well as mining difficulty have positive impacts on the Bitcoin exchange rate. Additional analysis suggests that the impact of mining difficulty diminishes as mining technology evolves.

Subfields: Economics and Econometrics, Information Systems

View Paper: https://www.semanticscholar.org/paper/d616975a126f94cf49975813d4641e9912221c1d

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Citations in the corpus

Cryptocurrencies as a financial asset: A systematic analysis (2019, background): “…Mode Decomposition Daily blockchain None 1,000 70 The technology and economic determinants of cryptocurrency exchange rates: the case of Bitcoin Li and Wang 2017 Price Market Efficiency VECM Model Daily bitcoincharts Exchange Rates 1,096 71 Some stylized facts of the Bitcoin market Bariviera…”
Testing for asymmetric nonlinear short- and long-run relationships between bitcoin, aggregate commodity and gold prices (2018, methodology): “Several studies have focused on price discovery in the Bitcoin market (Bouoiyour and Selmi, 2015; Ciaian et al., 2016; Li and Wang, 2017) via the application of an ARDL and, and mixed findings have emerged.”
The predictive power of public Twitter sentiment for forecasting cryptocurrency prices (2020, background): “, 2015), mining di culty (Li and Wang, 2017), the political situation of a country (e.”
Long- and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis (2018, background): “The findings of Li and Wang (2017), among others, suggest that measures of financial and macroeconomic activity are drivers of Bitcoin returns.”
Governance in the Blockchain Economy: A Framework and Research Agenda (2018, background): “Thus far, most academic research has focused on cryptocurrencies like Bitcoin (e.g., Böhme, Christin, Edelman, & Moore, 2015; Kazan, Tan, & Lim, 2015; Li & Wang, 2017; Nakamoto, 2008); however, at this point, blockchain has evolved beyond Bitcoin.”
Spillovers between Bitcoin and other assets during bear and bull markets (2018, background): “…on the part of computer programming enthusiasts (Yelowitz and Wilson 2015), cyber-attacks (Moore and Christin 2013) and cost of mining (Hayes 2016; Li and Wang 2017), it often is argued that the Bitcoin market is related very weakly to other global macroeconomic aggregates (Brière, Oosterlinck,…”
Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios (2019, background): “On a parallel note, and as far as speculation is concerned, evidence of the latter is emphasized in the work of Li and Wang (2017) who concentrate on the exchange rate between the Bitcoin and the USD.”