Abstract
We aim to understand the dynamics of Bitcoin blockchain trading volumes and, specifically, how different trading groups, in different geographic areas, interact with each other. To achieve this aim, we propose an extended Vector Autoregressive model, aimed at explaining the evolution of trading volumes, both in time and in space. The extension is based on network models, which improve pure autoregressive models, introducing a contemporaneous contagion component that describes contagion effects between trading volumes. Our empirical findings show that transactions activities in bitcoins is dominated by groups of network participants in Europe and in the United States, consistent with the expectation that market interactions primarily take place in developed economies.
Highlights
The bitcoin is the leading cryptocurrency by capitalisation, with a market share greater than 50%of the total cryptocurrency market, corresponding to 330 billion USD at its historical peak, in December2017
We proposed a model that explains the dynamics of Bitcoin trading volumes, based on a correlation network VAR process that models the interconnections between different groups of traders
Our main methodological contribution consists of the introduction of partial correlations and correlation networks into VAR models
Summary
The bitcoin is the leading cryptocurrency by capitalisation, with a market share greater than 50%of the total cryptocurrency market, corresponding to 330 billion USD at its historical peak, in December2017. Recent studies report that the same market capitalisation is concentrated on a limited number of owners. Credit Swiss in January 2018 provided a study which indicates that 97% of Bitcoins are held by 4% of all Bitcoin addresses. Bloomberg reported similar findings by suggesting that about 40 percent of Bitcoin is held by perhaps 1000 users. The previous empirical findings suggest that the trading movement by a few bitcoin owners has the potential to cause major disruptions in the price of all cryptocurrencies. An example of this is the transaction that took place on 12 November 2017, when a user moved 25,000 Bitcoins, worth at the time USD159 million, to an exchange. A very important research question is : “to find the bitcoin owners who are most connected in the markets, in terms of trading volumes”
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