Inferring short-term volatility indicators from Bitcoin blockchain
This work addresses the problem of forecasting financial risk in cryptocurrency markets for traders and analysts, but it is incremental as it builds on existing graph-based methods.
The paper tackled predicting extreme Bitcoin price volatility by using low-dimensional representations from daily transaction graphs, finding that their non-negative decomposition-based early warning indicator outperformed singular value decomposition and total transaction volume in predictive information.
In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockchain, and demonstrate how these representations can be used to predict extreme price volatility events. Our EWI, which is obtained with a non-negative decomposition, contains more predictive information than those obtained with singular value decomposition or scalar value of the total Bitcoin transaction volume.