ArticleEntropy (Basel, Switzerland)2020
Network Analysis of Multivariate Transfer Entropy of Cryptocurrencies in Times of Turbulence.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed, 33 citations in OpenAlex.
- Transfer Entropy Causal Networks for Interconnectedness Analysis of Global Banking and Green Markets: A CEEMDAN-SE-KM Approach.Entropy (Basel, Switzerland) · 2026Article
- Signed Connectedness Among Cryptocurrencies, NFTs, and Foreign Exchange Markets.Entropy (Basel, Switzerland) · 2026Article
- Information Theory Quantifiers in Cryptocurrency Time Series Analysis.Entropy (Basel, Switzerland) · 2025Article
- Measuring the Risk Spillover Effect of RCEP Stock Markets: Evidence from the TVP-VAR Model and Transfer Entropy.Entropy (Basel, Switzerland) · 2025Article
- COVID-19 Effects on the Relationship between Cryptocurrencies: Can It Be Contagion? Insights from Econophysics Approaches.Entropy (Basel, Switzerland) · 2023Article
- Diversity dependence is a ubiquitous phenomenon across Phanerozoic oceans.Science advances · 2022Article
- Dynamic Multiscale Information Spillover among Crude Oil Time Series.Entropy (Basel, Switzerland) · 2022Article
- Cryptocurrency Market Consolidation in 2020-2021.Entropy (Basel, Switzerland) · 2021Article
- Complexity in Economic and Social Systems.Entropy (Basel, Switzerland) · 2021Article
- Article
- Complexity in Economic and Social Systems: Cryptocurrency Market at around COVID-19.Entropy (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 2 countries.
Funding
Abstract
We investigate the effects of the recent financial turbulence of 2020 on the market of cryptocurrencies taking into account the hourly price and volume of transactions from December 2019 to April 2020. The data were subdivided into time frames and analyzed the directed network generated by the estimation of the multivariate transfer entropy. The approach followed here is based on a greedy algorithm and multiple hypothesis testing. Then, we explored the clustering coefficient and the degree distributions of nodes for each subperiod. It is found the clustering coefficient increases dramatically in March and coincides with the most severe fall of the recent worldwide stock markets crash. Further, the log-likelihood in all cases bent over a power law distribution, with a higher estimated power during the period of major financial contraction. Our results suggest the financial turbulence induce a higher flow of information on the cryptocurrency market in the sense of a higher clustering coefficient and complexity of the network. Hence, the complex properties of the multivariate transfer entropy network may provide early warning signals of increasing systematic risk in turbulence times of the cryptocurrency markets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.