Long-range correlations in cryptocurrency markets: A multi-scale DFA approach

Huy Quoc Bui, Christophe Schinckus and Hamdan Al-Jaifi

Physica A: Statistical Mechanics and its Applications, 2025, vol. 661, issue C

Abstract: This article investigates the long-range correlations within the cryptocurrency market by investigating the Hurst exponents across multiple time scales for the log-returns of the top five cryptocurrencies (capturing over 70 % of the market capitalization) between 2017 and 2023. The study uncovers several notable insights. An overall analysis indicates the presence of persistent long-range correlations in four out of five cryptocurrencies, with only XRP displaying characteristics of a random walk. A closer look differentiates the dynamics between short-term and long-term scales, revealing that ETH uniquely maintaining a strong persistence in both, unlike the other cryptocurrencies, which show varying behaviors across these scales. Additionally, ETH and XRP show persistent effects in times of market volatility. This reflects temporal patterns within cryptocurrency markets, enhancing the understanding of market behaviour across varying conditions and timescales. Our findings suggest opportunities for using Hurst exponents as tools to monitor trend continuation or reversal, develop asset-specific strategies, and detect systemic risks during extreme market conditions, offering valuable insights for traders and policymakers navigating the cryptocurrency market's inherent volatility

Keywords: Cryptocurrency market; Long-range correlation; DFA; Hurst exponents; Market crash (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:661:y:2025:i:c:s037843712500069x

DOI: 10.1016/j.physa.2025.130417

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