Abstract

The recent surge in Bitcoin price performance has attracted significant attention from both the market and academic researchers. This paper constitutes the first principled attempt to determine market risk own-funds requirements for Bitcoin. To this end, we examine price microstructure of the USD per bitcoin, and compare to other financial variables, as a proxy toward classifying Bitcoin into the appropriate risk-class. Using the outcomes of this analysis, we classify and quantify the entailed risk from a market risk minimum capital requirements perspective. To perform the prescribed analysis, we introduce a novel methodological paradigm, which adopts bleeding-edge concepts from the field of Data Science and Machine Learning.

Keywords: Bitcoin, Cryptocurrencies, Extreme gradient boosting, Deep neural networks