Pairwise and High-Order Information Flows in Cryptocurrency Markets

Summary

This study represents cryptocurrencies as nodes in a trading network and links them when weekly U.S. dollar log returns show Granger-causal relationships. It examines how those links and broader statistical dependencies changed during 2020 and 2021. Pairwise influence is measured with Granger causality, while high-order relationships are assessed with O-information, which helps distinguish redundancy from synergy among multiple assets.

The reported pairwise network shows activity peaks around major market events, including pandemic turmoil and sudden price rises, while its structure remains relatively stable across weekly windows. The most influential nodes are coins; stablecoins have limited importance in the pairwise view but feature prominently in synergistic high-order circuits. The authors also associate high transaction volumes in the first half of 2021 with a shift toward more complex network dynamics. The results offer complementary ways to study market dependence, but describe a particular period and depend on the selected return frequency and statistical measures; they do not establish a trading strategy or prove causal mechanisms beyond the network analysis.

Key ideas

• The study builds a cryptocurrency network from Granger-causal links between weekly log returns.
• Pairwise network activity peaks around major market events, while the observed structure is relatively stable across weekly windows.
• Stablecoins play a limited role in pairwise links but appear frequently in synergistic high-order relationships.
• High transaction volume in early 2021 coincides with a shift toward more complex network dynamics.
• Pairwise and high-order dependence measures capture complementary aspects of cryptocurrency markets.

Tags

# Pairwise and high-order dependencies in the cryptocurrency trading network


# Pairwise and high-order dependencies in the cryptocurrency trading network









In this paper we analyse the effects of information flows in cryptocurrency markets. We first define a cryptocurrency trading network, i.e. the network made using cryptocurrencies as nodes and the Granger causality among their weekly log returns as links, later we analyse its evolution over time. In particular, with reference to years 2020 and 2021, we study the logarithmic US dollar price returns of the cryptocurrency trading network using both pairwise and high-order statistical dependencies, quantified by Granger causality and O-information, respectively. With reference to the former, we find that it shows peaks in correspondence of important events, like e.g., Covid-19 pandemic turbulence or occasional sudden prices rise. The corresponding network structure is rather stable, across weekly time windows in the period considered and the coins are the most influential nodes in the network. In the pairwise description of the network, stable coins seem to play a marginal role whereas, turning high-order dependencies, they appear in the highest number of synergistic information circuits, thus proving that they play a major role for high order effects. With reference to redundancy and synergy with the time evolution of the total transactions in US dollars, we find that their large volume in the first semester of 2021 seems to have triggered a transition in the cryptocurrency network toward a more complex dynamical landscape. Our results show that pairwise and high-order descriptions of complex financial systems provide complementary information for cryptocurrency analysis.

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