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Filtering amplitude dependence of correlation dynamics in complex systems: application to the cryptocurrency market

Marcin Wątorek, Marija Bezbradica, Martin Crane, Jarosław Kwapień, Stanisław Drożdż

arXiv 23 Sep 2025 · Finance — Statistical Finance · publishedPhysical review. E (2025) · 1 citations (OpenAlex)

arXiv:2509.18820 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Based on the cryptocurrency market dynamics, this study presents a general methodology for analyzing evolving correlation structures in complex systems using the $q$-dependent detrended cross-correlation coefficient \rho(q,s). By extending traditional metrics, this approach captures correlations at varying fluctuation amplitudes and time scales. The method employs $q$-dependent minimum spanning trees ($q$MSTs) to visualize evolving network structures. Using minute-by-minute exchange rate data for 140 cryptocurrencies on Binance (Jan 2021-Oct 2024), a rolling window analysis reveals significant shifts in $q$MSTs, notably around April 2022 during the Terra/Luna crash. Initially centralized around Bitcoin (BTC), the network later decentralized, with Ethereum (ETH) and others gaining prominence. Spectral analysis confirms BTC's declining dominance and increased diversification among assets. A key finding is that medium-scale fluctuations exhibit stronger correlations than large-scale ones, with $q$MSTs based on the latter being more decentralized. Properly exploiting such facts may offer the possibility of a more flexible optimal portfolio construction. Distance metrics highlight that major disruptions amplify correlation differences, leading to fully decentralized structures during crashes. These results demonstrate $q$MSTs' effectiveness in uncovering fluctuation-dependent correlations, with potential applications beyond finance, including biology, social and other complex systems.

Citation extraction

114
references
165
in-text mentions
114
distinct cited
2
self-citations
9,744
main-text words

appendix boundary found by appendix_command · 92% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1author author J. Kwapień, author P. Oświecimka, author M. Forczek,\… (2017) ) NoStop0.87472100%
2author author J. Kwapień, author P. Oświecimka,\ and\ author S. Droż… (2015) ) NoStop0.87452100%
3author author J. Kwapień\ and\ author S. Drożdż,\ title title Physic… (2012) 01.007 journal journal Physics Reports\ volume 515,\ pages 115 ( year 2012) NoStop0.84333100%
4@noop title CoinDesk DACSclassification,\ howpublished https://indic…0.7373367%
5author author J. W.\ Kantelhardt, author S. A.\ Zschiegner, author E… (2002) ) NoStop0.73732100%
6author author R. N.\ Mantegna,\ title title Hierarchical structure i… (1999) ) NoStop0.73732100%
7author author P. Oświecimka, author S. Drożdż, author M. Forczek, au… (2014) ) NoStop0.73732100%
8author author S. Drożdż\ and\ author P. Oświecimka,\ title title Det… (2015) ) NoStop0.64422100%
9author author J. B.\ Kruskal,\ title title On the shortest spanning… (1956) ) NoStop0.64422100%
10author author R. C.\ Prim,\ title title Shortest connection networks… (1957) ) NoStop0.64422100%

Showing the top 10 of 114 scored citations.