Dynamic communities in multichannel data: An application to the foreign exchange market during the 2007-2008 credit crisis

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

Research output: Contribution to journalArticlepeer-review

60 Scopus citations

Abstract

We study the cluster dynamics of multichannel (multivariate) time series by representing their correlations as time-dependent networks and investigating the evolution of network communities. We employ a node-centric approach that allows us to track the effects of the community evolution on the functional roles of individual nodes without having to track entire communities. As an example, we consider a foreign exchange market network in which each node represents an exchange rate and each edge represents a time-dependent correlation between the rates. We study the period 2005-2008, which includes the recent credit and liquidity crisis. Using community detection, we find that exchange rates that are strongly attached to their community are persistently grouped with the same set of rates, whereas exchange rates that are important for the transfer of information tend to be positioned on the edges of communities. Our analysis successfully uncovers major trading changes that occurred in the market during the credit crisis.

Original languageEnglish (US)
Article number033119
JournalChaos
Volume19
Issue number3
DOIs
StatePublished - 2009

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics
  • Physics and Astronomy(all)
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Dynamic communities in multichannel data: An application to the foreign exchange market during the 2007-2008 credit crisis'. Together they form a unique fingerprint.

Cite this