Goh Yong Kheng, Hasim Haslifah M, Antonopoulos Chris G
Centre for Mathematical Sciences, Universiti Tunku Abdul Rahman, Kajang, Malaysia.
Department of Mathematical Sciences, University of Essex, Colchester, United Kingdom.
PLoS One. 2018 Feb 8;13(2):e0192160. doi: 10.1371/journal.pone.0192160. eCollection 2018.
In this paper, we study data from financial markets, using the normalised Mutual Information Rate. We show how to use it to infer the underlying network structure of interrelations in the foreign currency exchange rates and stock indices of 15 currency areas. We first present the mathematical method and discuss its computational aspects, and apply it to artificial data from chaotic dynamics and to correlated normal-variates data. We then apply the method to infer the structure of the financial system from the time-series of currency exchange rates and stock indices. In particular, we study and reveal the interrelations among the various foreign currency exchange rates and stock indices in two separate networks, of which we also study their structural properties. Our results show that both inferred networks are small-world networks, sharing similar properties and having differences in terms of assortativity. Importantly, our work shows that global economies tend to connect with other economies world-wide, rather than creating small groups of local economies. Finally, the consistent interrelations depicted among the 15 currency areas are further supported by a discussion from the viewpoint of economics.
在本文中，我们使用归一化互信息率来研究金融市场数据。我们展示了如何利用它来推断15个货币区的外汇汇率和股票指数之间潜在的网络结构关系。我们首先介绍数学方法并讨论其计算方面，然后将其应用于来自混沌动力学的人工数据以及相关正态变量数据。接着，我们运用该方法从货币汇率和股票指数的时间序列中推断金融系统的结构。特别地，我们研究并揭示了两个独立网络中各种外汇汇率和股票指数之间的相互关系，同时还研究了它们的结构特性。我们的结果表明，两个推断出的网络都是小世界网络，具有相似的属性，但在度分布方面存在差异。重要的是，我们的研究表明全球经济倾向于与全球其他经济体建立联系，而非形成小范围的本地经济体集群。最后，从经济学角度的讨论进一步支持了15个货币区之间所呈现出的一致相互关系。
