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Updated: Aug 23, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
Published on: July 14, 2015
Pairwise and high-order dependencies in the cryptocurrency trading network
Tomas Scagliarini1,2, Giuseppe Pappalardo3, Alessio Emanuele Biondo4
1Dipartimento Interateneo di Fisica, Università degli Studi Aldo Moro, Bari, Italy. tomas.scagliarini@uniba.it.
This study reveals how information flows shape cryptocurrency markets. Stablecoins play a crucial role in complex information circuits, influencing market dynamics during significant events and transaction surges.
Area of Science:
- * Financial network analysis
- * Cryptocurrency market dynamics
- * Information flow studies
Background:
- * Understanding information flow is key to analyzing cryptocurrency market behavior.
- * Previous analyses often overlook high-order dependencies and the role of stablecoins.
Purpose of the Study:
- * To analyze the effects of information flows in cryptocurrency markets.
- * To investigate the evolution of the cryptocurrency trading network over time.
- * To compare pairwise and high-order statistical dependencies in market analysis.
Main Methods:
- * Construction of a cryptocurrency trading network using Granger causality on weekly log returns.
- * Analysis of network evolution during 2020-2021 using pairwise (Granger causality) and high-order (O-information) dependencies.
- * Examination of the relationship between network dynamics and total US dollar transaction volumes.
Main Results:
- * Granger causality peaks during major market events (e.g., COVID-19, price surges).
- * Stablecoins, marginal in pairwise analysis, are central to high-order synergistic information circuits.
- * High transaction volumes in early 2021 correlated with increased network complexity.
Conclusions:
- * Pairwise and high-order analyses offer complementary insights into cryptocurrency markets.
- * Stablecoins are critical for understanding complex, high-order information dynamics.
- * Market events and transaction volumes significantly impact network structure and complexity.
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