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Discrete Dynamics in Nature and Society
Volume 2017, Article ID 5632374, 17 pages
https://doi.org/10.1155/2017/5632374
Research Article

A Visibility Graph Approach to CNY Exchange Rate Networks and Characteristic Analysis

1School of Economics and Commerce, South China University of Technology, Guangzhou 510006, China
2Physics Department, University of Fribourg, Chemin du Musée 3, 1700 Fribourg, Switzerland
3Department of Economics, The Chinese University of Hong Kong, Shatin, Hong Kong

Correspondence should be addressed to Can-Zhong Yao; moc.liamg@11902102zcy

Received 15 April 2017; Revised 19 August 2017; Accepted 24 October 2017; Published 29 November 2017

Academic Editor: J. R. Torregrosa

Copyright © 2017 Can-Zhong Yao and Ji-Nan Lin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We find that exchange rate networks are significantly similar from the perspective of topological structure, though with relatively great differences in fluctuation characteristics from perspective of exchange rate time series. First, we transform central parity rate time series of US dollar, Euro, Yen, and Sterling against CNY into exchange rate networks with visibility graph algorithm and find consistent topological characteristics in four exchange rate networks, with their average path lengths 5 and average clustering coefficients 0.7. Further, we reveal that all four transformed exchange rate networks show hierarchical structure and small-world and scale-free properties, with their hierarchy indexes 0.5 and power exponents 1.5. Both of the US dollar network and Sterling network exhibit assortative mixing features, while the Euro network and Yen network exhibit disassortative mixing features. Finally, we research community structure of exchange rate networks and uncover the fact that the communities are actually composed by large amounts of continuous time point fractions and small amounts of discrete time point fractions. In this way, we can observe that the spread of time series values corresponding to nodes inside communities is significantly lower than the spread of those values corresponding to nodes of the whole networks.