Dynamic Properties of Foreign Exchange Complex Network
The foreign exchange (FX) market, one of the important components of the financial market, is a typical complex system. In this paper, by resorting to the complex network method, we use the daily closing prices of 41 FX markets to build the dynamical networks and their minimum spanning tree (MST) ma...
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doaj-d5e4c08f500e4987a23bb4d9747a862f2020-11-25T01:01:40ZengMDPI AGMathematics2227-73902019-09-017983210.3390/math7090832math7090832Dynamic Properties of Foreign Exchange Complex NetworkXin Yang0Shigang Wen1Zhifeng Liu2Cai Li3Chuangxia Huang4School of Mathematics and Statistics, Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaSchool of Mathematics and Statistics, Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaSchool of Management, Hainan University, Haikou 570228, ChinaSchool of Mathematics and Statistics, Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaSchool of Mathematics and Statistics, Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaThe foreign exchange (FX) market, one of the important components of the financial market, is a typical complex system. In this paper, by resorting to the complex network method, we use the daily closing prices of 41 FX markets to build the dynamical networks and their minimum spanning tree (MST) maps by virtue of a moving window correlation coefficient. The properties of FX networks are characterized by the normalized tree length, node degree distributions, centrality measures and edge survival ratios. Empirical results show that: (i) the normalized tree length plays a role in identifying crises and is negatively correlated with the market return and volatility; (ii) 83% of FX networks follow power-law node degree distribution, which means that the FX market is a typical heterogeneous market, and a few hub nodes play key roles in the market; (iii) the highest centrality measures reveal that the USD, EUR and CNY are the three most powerful currencies in FX markets; and (iv) the edge survival ratio analysis implies that the FX structure is relatively stable.https://www.mdpi.com/2227-7390/7/9/832foreign exchange marketscomplex networkminimum spanning treemarket phenomena |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xin Yang Shigang Wen Zhifeng Liu Cai Li Chuangxia Huang |
spellingShingle |
Xin Yang Shigang Wen Zhifeng Liu Cai Li Chuangxia Huang Dynamic Properties of Foreign Exchange Complex Network Mathematics foreign exchange markets complex network minimum spanning tree market phenomena |
author_facet |
Xin Yang Shigang Wen Zhifeng Liu Cai Li Chuangxia Huang |
author_sort |
Xin Yang |
title |
Dynamic Properties of Foreign Exchange Complex Network |
title_short |
Dynamic Properties of Foreign Exchange Complex Network |
title_full |
Dynamic Properties of Foreign Exchange Complex Network |
title_fullStr |
Dynamic Properties of Foreign Exchange Complex Network |
title_full_unstemmed |
Dynamic Properties of Foreign Exchange Complex Network |
title_sort |
dynamic properties of foreign exchange complex network |
publisher |
MDPI AG |
series |
Mathematics |
issn |
2227-7390 |
publishDate |
2019-09-01 |
description |
The foreign exchange (FX) market, one of the important components of the financial market, is a typical complex system. In this paper, by resorting to the complex network method, we use the daily closing prices of 41 FX markets to build the dynamical networks and their minimum spanning tree (MST) maps by virtue of a moving window correlation coefficient. The properties of FX networks are characterized by the normalized tree length, node degree distributions, centrality measures and edge survival ratios. Empirical results show that: (i) the normalized tree length plays a role in identifying crises and is negatively correlated with the market return and volatility; (ii) 83% of FX networks follow power-law node degree distribution, which means that the FX market is a typical heterogeneous market, and a few hub nodes play key roles in the market; (iii) the highest centrality measures reveal that the USD, EUR and CNY are the three most powerful currencies in FX markets; and (iv) the edge survival ratio analysis implies that the FX structure is relatively stable. |
topic |
foreign exchange markets complex network minimum spanning tree market phenomena |
url |
https://www.mdpi.com/2227-7390/7/9/832 |
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