The technology and economic determinants of cryptocurrency exchange rates: The case of Bitcoin
We theoretically discuss the technology
and economic determinants of the Bitcoin exchange rate We use the ARDL model with bounds test to address co-integration of a mix of stationary and non-stationary time series We find Bitcoin exchange rate relates more with economic fundamentals and less with technology factors as Bitcoin evolves We find the impact of computational capacities on Bitcoin is decreasing as technology progresses
Cryptocurrencies, such as Bitcoin, have ignited intense discussions. Despite receiving extensive public attention, theoretical understanding is limited regarding the value of blockchain-based cryptocurrencies, as expressed in their exchange rates against traditional currencies. In this paper, we conduct a theory-driven empirical study of the Bitcoin exchange rate (against USD) determination, taking into consideration both technology and economic factors. To address co-integration in a mix of stationary and non-stationary time series, we use the autoregressive distributed lag (ARDL) model with a bounds test approach in the estimation. Meanwhile, to detect potential structural changes, we estimate our empirical model on two periods separated by the closure of Mt. Gox (one of the largest Bitcoin exchange markets). According to our analysis, in the short term, the Bitcoin exchange rate adjusts to changes in economic fundamentals and market conditions. The long-term Bitcoin exchange rate is more sensitive to economic fundamentals and less sensitive to technological factors after Mt. Gox closed. We also identify a significant impact of mining technology and a decreasing significance of mining difficulty in the Bitcoin exchange price determination.
is an associate professor in the Department of Information Systems at the City University of Hong Kong. He received his Ph.D. in Management Information Systems from the University of Arizona. He received his Bachelor's and Master's degrees from the Department of Automation at Tsinghua University, China. His research interests include business intelligence & knowledge discovery, social network analysis, social media, and applied econometrics. His work has appeared in the MIS Quarterly, INFORMS Journal on Computing, Journal of Management Information Systems, Decision Support Systems, Journal of the American Society for Information Science and Technology, ACM Transactions on Management Information Systems, IEEE Intelligent Systems, among others, and in various conference proceedings.
is an assistant professor in the Department of Information Systems at the City University of Hong Kong. He received his Ph.D. in Information Systems from the Hong Kong University of Science and Technology. He received his Master's degrees from the Department of Finance at Tsinghua University, China, and his Bachelor's degree from the Department of Applied Mathematics at Peking University, China. His research focuses on understanding the social and economic impacts of information technology. His research projects cover topics in the areas of online social networks, crowdsourcing platforms, and financial information technologies. His work has appeared in the Information Systems Research, Journal of Management Information Systems, Decision Support Systems, and in various conference proceedings.
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