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

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. (c) 2022 Author(s).

Automated summary

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