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...

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...

Bitcoin blockchain has grown into an active global virtual money network with millions of accounts.We propose a Sparse-Group Network AutoRegressive (SGNAR) model to understand the dynamics of its cross-border transactions. It describes the money flows of virtual funds, with a focus on the...

Understanding multi-market interactions and identifying leading markets in the global financial network is of interest to investors, regulators and policymakers. To discover the essential dynamic dependencies of digital currency exchanges, we propose TriSNAR, a three-layer sparse estimator for...

We present a TriSNAR modeling framework for understanding the dynamic interactions of multiple markets for Bitcoin trading, including market efficiency, and for identifying influential exchanges in the global trading network. We consider two types of influential exchanges from the perspectives...

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 method to Detect Influencers in Network AutoRegressive models (DINAR) via sparse-group...