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

TL;DR

This paper investigates the dynamic information flow between major Bitcoin markets during the 2017-2018 bubble using high-frequency data and information theoretic measures, revealing temporal shifts in predictability, memory, and coupling.

Contribution

It introduces a novel analysis of intra- and inter-market information dynamics during a cryptocurrency market crash using advanced information theoretic tools.

Findings

Detected temporal changes in information sharing across markets.

Identified regime shifts and changes in information flow direction.

Observed variations in predictability, memory, and coupling over time.

Abstract

We study the information dynamics between the largest Bitcoin exchange markets during the bubble in 2017-2018. By analysing 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 the time-varying components of predictability, memory, and 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 direction of information flow between different market observables.