Cryptocurrency Trading Activity, Inter-Trade Times, and Multifractality

Summary

This analysis examines tick-level data from major cryptocurrencies traded on multiple platforms. It studies the time between transactions alongside transaction counts, trading volume, and volatility. The authors report long-range power-law autocorrelation in inter-transaction times and relate this pattern to multifractality. The singularity spectra are asymmetric, with richer multifractal behavior during high-activity periods than during quieter periods.

The study also compares candidate distributions for the observed quantities. Neither stretched exponential nor power-law-tail distributions fit all platform and quantity combinations: one may work in some cases, while both fail in others. Parallel datasets from different platforms can have markedly different statistical properties. The excerpt does not identify the platforms, sampling period, or a universal model, so the findings point to venue-specific behavior and limits on broad distributional assumptions rather than a single trading rule.

Key ideas

• Inter-transaction times in the studied crypto data show long-range power-law autocorrelation.
• High-activity periods exhibit richer multifractality than quiet periods.
• Stretched exponential and power-law-tail distributions do not fit every measured quantity or dataset.
• Datasets from different platforms can have substantially different statistical properties.
• The excerpt provides market-structure observations rather than a specified trading strategy.

Tags

# Analysis of inter-transaction time fluctuations in the cryptocurrency market


# Analysis of inter-transaction time fluctuations in the cryptocurrency market









We analyse tick-by-tick data representing major cryptocurrencies traded on some different cryptocurrency trading platforms. We focus on such quantities like the inter-transaction times, the number of transactions in time unit, the traded volume, and volatility. We show that the inter-transaction times show long-range power-law autocorrelations. These lead to multifractality expressed by the right-side asymmetry of the singularity spectra $f(α)$ indicating that the periods of increased market activity are characterised by richer multifractality compared to the periods of quiet market. We also show that neither the stretched exponential distribution nor the power-law-tail distribution are able to model universally the cumulative distribution functions of the quantities considered in this work. For each quantity, some data sets can be modeled by the former, some data sets by the latter, while both fail in other cases. An interesting, yet difficult to account for, observation is that parallel data sets from different trading platforms can show disparate statistical properties.

Shown in full with attribution under the source's licence. Licence: abstract CC0

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