Multifractal cross-correlations of bitcoin and ether trading characteristics in the post-COVID-19 time

TL;DR

This study investigates the multifractal cross-correlations between bitcoin and ether trading characteristics using high-frequency data, revealing persistent nonlinear correlations and multifractal structures in their temporal trading dynamics post-COVID-19.

Contribution

It applies multifractal detrended cross-correlation analysis to cryptocurrency trading data, uncovering complex nonlinear cross-correlations and their persistence over different time scales.

Findings

All trading quantities exhibit multifractal structures.

Cross-correlations persist even with time shifts.

Long-term cross-correlations are symmetric between assets.

Abstract

Unlike price fluctuations, the temporal structure of cryptocurrency trading has seldom been a subject of systematic study. In order to fill this gap, we analyse detrended correlations of the price returns, the average number of trades in time unit, and the traded volume based on high-frequency data representing two major cryptocurrencies: bitcoin and ether. We apply the multifractal detrended cross-correlation analysis, which is considered the most reliable method for identifying nonlinear correlations in time series. We find that all the quantities considered in our study show an unambiguous multifractal structure from both the univariate (auto-correlation) and bivariate (cross-correlation) perspectives. We looked at the bitcoin--ether cross-correlations in simultaneously recorded signals, as well as in time-lagged signals, in which a time series for one of the cryptocurrencies is shifted with respect to the other. Such a shift suppresses the cross-correlations partially for short time scales, but does not remove them completely. We did not observe any qualitative asymmetry in the results for the two choices of a leading asset. The cross-correlations for the simultaneous and lagged time series became the same in magnitude for the sufficiently long scales.