Bitcoin Risk Modeling with Blockchain Graphs

TL;DR

This paper introduces a novel graph-based modeling approach for Bitcoin transaction networks to analyze and predict cryptocurrency investment risks, focusing on network structures and transaction patterns.

Contribution

It presents a new microstructure modeling method using blockchain graph topologies to understand and forecast Bitcoin price movements and volatility.

Findings

Identification of chainlet sub-graphs that influence Bitcoin price and volatility

Characterization of chainlets associated with extreme losses

Demonstration of network topology's role in crypto risk dynamics

Abstract

A key challenge for Bitcoin cryptocurrency holders, such as startups using ICOs to raise funding, is managing their FX risk. Specifically, a misinformed decision to convert Bitcoin to fiat currency could, by itself, cost USD millions. In contrast to financial exchanges, Blockchain based crypto-currencies expose the entire transaction history to the public. By processing all transactions, we model the network with a high fidelity graph so that it is possible to characterize how the flow of information in the network evolves over time. We demonstrate how this data representation permits a new form of microstructure modeling - with the emphasis on the topological network structures to study the role of users, entities and their interactions in formation and dynamics of crypto-currency investment risk. In particular, we identify certain sub-graphs ('chainlets') that exhibit predictive influence on Bitcoin price and volatility, and characterize the types of chainlets that signify extreme losses.