Multifractal Cross-Correlations in Bitcoin and Ether Trading Activity

Summary

This study examines high-frequency trading characteristics for Bitcoin and Ether in the post-COVID period. It analyzes returns, the average number of trades per time unit, and traded volume using multifractal detrended cross-correlation analysis, a method suited to detecting nonlinear dependence across time scales. The analysis considers both each series on its own and relationships between the two cryptocurrencies.

The authors report multifractal structure across all measures, including cross-correlations between the assets. Shifting one asset’s series in time partially weakens cross-correlations at short scales, but does not eliminate them; no qualitative difference appears depending on which asset is treated as leading. At sufficiently long scales, simultaneous and lagged cross-correlations reach the same magnitude. The document describes statistical findings rather than a trading strategy, and it provides no predictive tests, sample specifics, or evidence that the measured dependence can be converted into net returns.

Key ideas

• The analysis covers returns, trade counts, and traded volume for Bitcoin and Ether.
• Multifractal detrended cross-correlation analysis is used to study nonlinear time-series dependence.
• The authors find multifractal structure in both individual series and cross-asset relationships.
• Time-shifting one asset partially reduces short-scale cross-correlations without removing them.
• At long scales, simultaneous and lagged cross-correlations converge in magnitude, with no qualitative lead-direction asymmetry reported.

Tags

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


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









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.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.