A1781
Title: Influencers, inefficiency and fraud: The Bitcoin price discovery network under the microscope
Authors: Simon Trimborn - University of Amsterdam (Netherlands) [presenting]
Ying Chen - National University of Singapore (Singapore)
Ray-Bing Chen - National Cheng Kung University (Taiwan)
Abstract: A TriSNAR modelling framework is presented for understanding the dynamic interactions of multiple markets for Bitcoin trading, including market efficiency, and for identifying influential exchanges in the global trading network. It is of interest to identify exchanges that are market leaders. Out of 339 weeks (6.5 years of data), 104 weeks are identified in which TriSNAR provides the best MSFE out of 6 contestants and significantly outperforms all other models. Among 194 Bitcoin exchanges, it is found that exchange Kraken was the leading exchange prior to the market frenzy of 2017, in particular in 2016. In addition, price discovery shows that the Bitcoin exchange network efficiency decreased from 2015 to 2017, and increased since 2018. The relation is analysed between blockchain fund flows and influential exchanges, and it is observed that wealthy holders of Bitcoin transact funds to exchanges when influential exchanges arise. The finite sample and asymptotic properties of TriSNAR are investigated. Compared to alternative methods, TriSNAR outperforms in terms of accuracy and ability to discover multi-market network structures.
