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加密货币市场的成对与高阶信息流

文章 arXiv papers · 作者: Tomas Scagliarini et al.
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总结

本研究将加密货币表示为交易网络中的节点，并在每周美元对数收益显示格兰杰因果关系时连接这些节点。研究考察了这些联系及更广泛统计依赖关系在 2020 年和 2021 年间的变化。成对影响通过格兰杰因果关系衡量；高阶关系则通过 O 信息量评估，该指标有助于区分多个资产间的冗余与协同。

报告的成对网络显示，重大市场事件前后活动达到峰值，包括疫情动荡和价格突然上涨；但其结构在不同周度窗口间相对稳定。影响力最大的节点是加密货币；稳定币在成对关系中作用有限，却频繁出现在高阶协同回路中。作者还将 2021 年上半年的高交易量与网络动态转向更复杂联系起来。结果提供了研究市场依赖关系的互补方法，但描述的是特定时期，且取决于所选收益频率和统计指标；它们并未建立交易策略，也不能证明网络分析之外的因果机制。

核心观点

  • 本研究根据每周对数收益之间的格兰杰因果关系构建加密货币网络。
  • 成对网络活动在重大市场事件前后达到峰值，但其结构在不同周度窗口间相对稳定。
  • 稳定币在成对联系中的作用有限，但常出现在高阶协同关系中。
  • 2021年早期的高交易量与网络动态转向更复杂的情形同时出现。
  • 成对和高阶依赖指标能够揭示加密货币市场的不同方面。

标签

加密资产统计学市场微观结构
全文
# 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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。