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.
Remittance, one money flow between immigrants and their relatives, is a major source of foreign exchange revenue for economies. Consisting of economies linked by the money flows, global remittance constitutes a network. In this paper, we use bilateral remittances of 210 economies for the time period 2010-2016 to construct a global remittance network (GRN) and then investigate the network's structural properties. We study the degree distribution of the network and find that it is of heterogeneity. Analyses of centrality measures reveal that some key economies, such as the United States, France, India and China, are always ranked the highest. We also detect 6 communities in the network, where economies in the same regional economy cooperative organizations tend to be classified in the same community. Intra-community flows account for 66.07% of total remittances, indicating that economies present the characteristic of regionalization. In addition, the results of the topological stability test show that GRN is fragile to node removal, particular the selective removal based on betweenness centrality.
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