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Currency co-movement and network correlation structure of foreign exchange market

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  • Mai, Yong
  • Chen, Huan
  • Zou, Jun-Zhong
  • Li, Sai-Ping

Abstract

We study the correlations of exchange rate volatility in the global foreign exchange(FX) market based on complex network graphs. Correlation matrices (CM) and the theoretical information flow method (Infomap) are employed to analyze the modular structure of the global foreign exchange network. The analysis demonstrates that there exist currency modules in the network, which is consistent with the geographical nature of currencies. The European and the East Asian currency modules in the FX network are most significant. We introduce a measure of the impact of individual currency based on its partial correlations with other currencies. We further incorporate an impact elimination method to filter out the impact of core nodes and construct subnetworks after the removal of these core nodes. The result reveals that (i) the US Dollar has prominent global influence on the FX market while the Euro has great impact on European currencies; (ii) the East Asian currency module is more strongly correlated than the European currency module. The strong correlation is a result of the strong co-movement of currencies in the region. The co-movement of currencies is further used to study the formation of international monetary bloc and the result is in good agreement with the consideration based on international trade.

Suggested Citation

  • Mai, Yong & Chen, Huan & Zou, Jun-Zhong & Li, Sai-Ping, 2018. "Currency co-movement and network correlation structure of foreign exchange market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 65-74.
    • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:65-74
    DOI: 10.1016/j.physa.2017.09.068
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    Citations

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    Cited by:

    1. Bilal Ahmed Memon & Rabia Tahir, 2021. "Examining Network Structures and Dynamics of World Energy Companies in Stock Markets: A Complex Network Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 329-344.
    2. Basnarkov, Lasko & Stojkoski, Viktor & Utkovski, Zoran & Kocarev, Ljupco, 2020. "Lead–lag relationships in foreign exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    3. Wen, Tiange & Wang, Gang-Jin, 2020. "Volatility connectedness in global foreign exchange markets," Journal of Multinational Financial Management, Elsevier, vol. 54(C).
    4. Basnarkov, Lasko & Stojkoski, Viktor & Utkovski, Zoran & Kocarev, Ljupco, 2019. "Correlation patterns in foreign exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1026-1037.
    5. Zuzana Rowland & George Lazaroiu & Ivana Podhorská, 2020. "Use of Neural Networks to Accommodate Seasonal Fluctuations When Equalizing Time Series for the CZK/RMB Exchange Rate," Risks, MDPI, vol. 9(1), pages 1-21, December.
    6. Marek Vochozka & Jakub Horák & Petr Šuleř, 2019. "Equalizing Seasonal Time Series Using Artificial Neural Networks in Predicting the Euro–Yuan Exchange Rate," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    7. Al-Shboul, Mohammad & Assaf, Ata & Mokni, Khaled, 2023. "Does economic policy uncertainty drive the dynamic spillover among traditional currencies and cryptocurrencies? The role of the COVID-19 pandemic," Research in International Business and Finance, Elsevier, vol. 64(C).
    8. Jiang, Xue & Li, Sai-Ping & Mai, Yong & Tian, Tao, 2022. "Study of multinational currency co-movement and exchange rate stability base on network game," Finance Research Letters, Elsevier, vol. 47(PA).
    9. Anokye M. Adam & Kwabena Kyei & Simiso Moyo & Ryan Gill & Emmanuel N. Gyamfi, 2022. "Multifrequency network for SADC exchange rate markets using EEMD-based DCCA," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(1), pages 145-166, January.
    10. Yang, Ming-Yuan & Wu, Zhen-Guo & Wu, Xin, 2022. "An empirical study of risk diffusion in the cryptocurrency market based on the network analysis," Finance Research Letters, Elsevier, vol. 50(C).
    11. Xin Yang & Shigang Wen & Zhifeng Liu & Cai Li & Chuangxia Huang, 2019. "Dynamic Properties of Foreign Exchange Complex Network," Mathematics, MDPI, vol. 7(9), pages 1-19, September.

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