Discover Regional and Size Effects in Global Bitcoin Blockchain via Sparse-Group Network AutoRegressive Modeling

Abstract

Known as an active global virtual money network, Bitcoin blockchain with millions of accounts has played an ever growing important role in fund transition, digital payment and hedging. We propose a Sparse-Group Network AutoRegressive (SGNAR) model to understand the dynamics of its cross-border transactions and demonstrate the regional and size effects in the network. With two-layer sparsity, it enables discovering 1) the active regions with influential impact on the global digital money network and 2) the size of the groups which lead the dynamic evolution of Bitcoin transactions. We illustrate the finite sample performance of SGNAR along with intensive simulation studies. In the real data analysis on Bitcoin blockchain from Feb 2012 to July 2017, South America stood out as a leading region with a strong statistical significance. Meanwhile, the accounts with the smallest transaction size and the largest size were driving the Bitcoin transactions originated from South America, Europe, North America, Africa and Asia, while the other groups and all the accounts in Oceania were mostly followers. Moreover, the global connectivity was high in 2012 and enhanced in the years of 2016 and 2017.

Keywords: Bitcoin Blockchain, Network Dynamics, Two-Layer Sparsity

JEL Classification: C55, C58, C60, G17