Multiscale characteristics of the emerging global cryptocurrency market
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Abstract (arXiv)
The review introduces the history of cryptocurrencies, offering a description of the blockchain technology behind them. Differences between cryptocurrencies and the exchanges on which they are traded have been shown. The central part surveys the analysis of cryptocurrency price changes on various platforms. The statistical properties of the fluctuations in the cryptocurrency market have been compared to the traditional markets. With the help of the latest statistical physics methods the non-linear correlations and multiscale characteristics of the cryptocurrency market are analyzed. In the last part the co-evolution of the correlation structure among the 100 cryptocurrencies having the largest capitalization is retraced. The detailed topology of cryptocurrency network on the Binance platform from bitcoin perspective is also considered. Finally, an interesting observation on the Covid-19 pandemic impact on the cryptocurrency market is presented and discussed: recently we have witnessed a "phase transition" of the cryptocurrencies from being a hedge opportunity for the investors fleeing the traditional markets to become a part of the global market that is substantially coupled to the traditional financial instruments like the currencies, stocks, and commodities. The main contribution is an extensive demonstration that structural self-organization in the cryptocurrency markets has caused the same to attain complexity characteristics that are nearly indistinguishable from the Forex market at the level of individual time-series. However, the cross-correlations between the exchange rates on cryptocurrency platforms differ from it. The cryptocurrency market is less synchronized and the information flows more slowly, which results in more frequent arbitrage opportunities. The methodology used in the review allows the latter to be detected, and lead-lag relationships to be discovered.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 2; 2025: 1.
4 papers.
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- 2024Modelling crypto markets by multi-agent reinforcement learningcited by 0
- 2025Ethereum Price Prediction Employing Large Language Models for Short-term and Few-shot Forecasting29 Mar 2025
- 2024Modelling crypto markets by multi-agent reinforcement learning16 Feb 2024
- 2024Complex systems approach to natural language5 Jan 2024
- 2021Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-193 Jan 2021