Information dynamics of price and liquidity around the 2017 Bitcoin markets crash

Abstract

We study information dynamics between the largest Bitcoin exchange markets during the bubble in 2017–2018. By analyzing high-frequency market microstructure observables with different information-theoretic measures for dynamical systems, we find temporal changes in information sharing across markets. In particular, we study time-varying components of predictability, memory, and (a)synchronous coupling, measured by transfer entropy, active information storage, and multi-information. By comparing these empirical findings with several models, we argue that some results could relate to intra-market and inter-market regime shifts and changes in the direction of information flow between different market observables.