Dynamical clustering of exchange rates

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

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1493-1520
Number of pages28
JournalQuantitative Finance
Issue number10
StatePublished - Oct 2012
Externally publishedYes


  • Community detection
  • Foreign exchange market
  • Networks

ASJC Scopus subject areas

  • Finance
  • Economics, Econometrics and Finance(all)


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