ArticleNonlinear dynamics2022
Collective correlations, dynamics, and behavioural inconsistencies of the cryptocurrency market over time.
Article in Nonlinear dynamics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Detecting Unusual Trading Patterns on Cryptocurrency Exchanges by Means of Complexity Measures.Entropy (Basel, Switzerland) · 2026Article
- Digital assets: risks, regulations, mitigation.Financial innovation · 2026Article
- Detrended Cross-Correlations and Their Random Matrix Limit: An Example from the Cryptocurrency Market.Entropy (Basel, Switzerland) · 2025Article
- Collective Dynamics, Diversification and Optimal Portfolio Construction for Cryptocurrencies.Entropy (Basel, Switzerland) · 2023Article
- What Is Mature and What Is Still Emerging in the Cryptocurrency Market?Entropy (Basel, Switzerland) · 2023Article
- Cryptocurrencies Are Becoming Part of the World Global Financial Market.Entropy (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
This paper introduces new methods to study behaviours among the 52 largest cryptocurrencies between 01-01-2019 and 30-06-2021. First, we explore evolutionary correlation behaviours and apply a recently proposed turning point algorithm to identify regimes in market correlation. Next, we inspect the relationship between collective dynamics and the cryptocurrency market size-revealing an inverse relationship between the size of the market and the strength of collective dynamics. We then explore the time-varying consistency of the relationships between cryptocurrencies' size and their returns and volatility. There, we demonstrate that there is greater consistency between size and volatility than size and returns. Finally, we study the spread of volatility behaviours across the market changing with time by examining the structure of Wasserstein distances between probability density functions of rolling volatility. We demonstrate a new phenomenon of increased uniformity in volatility during market crashes, which we term
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