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Dynamical clustering of exchange rates


  • Daniel J. Fenn
  • Mason A. Porter
  • Peter J. Mucha
  • Mark McDonald
  • Stacy Williams
  • Neil F. Johnson
  • Nick S. Jones


We use techniques from network science to study correlations in the foreign exchange (FX) market during the period 1991--2008. We consider an FX market network in which each node represents an exchange rate and each weighted edge represents a time-dependent correlation between the rates. To provide insights into the clustering of the exchange-rate time series, we investigate dynamic communities in the network. We show that there is a relationship between an exchange rate's functional role within the market and its position within its community and use a node-centric community analysis to track the temporal dynamics of such roles. This reveals which exchange rates dominate the market at particular times and also identifies exchange rates that experienced significant changes in market role. We also use the community dynamics to uncover major structural changes that occurred in the FX market. Our techniques are general and will be similarly useful for investigating correlations in other markets.

Suggested Citation

  • Daniel J. Fenn & Mason A. Porter & Peter J. Mucha & Mark McDonald & Stacy Williams & Neil F. Johnson & Nick S. Jones, 2012. "Dynamical clustering of exchange rates," Quantitative Finance, Taylor & Francis Journals, vol. 12(10), pages 1493-1520, October.
  • Handle: RePEc:taf:quantf:v:12:y:2012:i:10:p:1493-1520 DOI: 10.1080/14697688.2012.668288

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    References listed on IDEAS

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    Cited by:

    1. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485,, revised Sep 2017.
    2. Nicolo Musmeci & Tomaso Aste & Tiziana Di Matteo, 2014. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," Papers 1406.0496,, revised Jan 2015.
    3. Marya Bazzi & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2014. "Community detection in temporal multilayer networks, with an application to correlation networks," Papers 1501.00040,, revised Dec 2017.
    4. Huang, Xuan & An, Haizhong & Gao, Xiangyun & Hao, Xiaoqing & Liu, Pengpeng, 2015. "Multiresolution transmission of the correlation modes between bivariate time series based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 493-506.
    5. Assaf Almog & Ferry Besamusca & Mel MacMahon & Diego Garlaschelli, 2015. "Mesoscopic Community Structure of Financial Markets Revealed by Price and Sign Fluctuations," Papers 1504.00590,
    6. Musmeci, Nicoló & Aste, Tomaso & Di Matteo, T., 2015. "Relation between financial market structure and the real economy: comparison between clustering methods," LSE Research Online Documents on Economics 61644, London School of Economics and Political Science, LSE Library.
    7. Nicol'o Musmeci & Tomaso Aste & Tiziana Di Matteo, 2014. "Risk diversification: a study of persistence with a filtered correlation-network approach," Papers 1410.5621,
    8. Itir OZER-IMER & Ibrahim OZKAN, 2013. "On the Co-Movements of Exchange Rates," Chapters of Financial Aspects of Recent Trends in the Global Economy book,in: Rajmund Mirdala (ed.), Financial Aspects of Recent Trends in the Global Economy, volume 2, chapter 1, pages 12-37 ASERS Publishing.
    9. Musmeci, Nicoló & Nicosia, Vincenzo & Aste, Tomaso & Di Matteo, Tiziana & Latora, Vito, 2017. "The multiplex dependency structure of financial markets," LSE Research Online Documents on Economics 85337, London School of Economics and Political Science, LSE Library.

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