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

The title of this article, which was just officially published today, is a bit of a mouthful. This paper is the first foray by my research group into financial networks. We posted its sequel, which has a lot more detail and examples, on the arXiv in May. We don't have referee reports for that one yet, which we submitted to SIAM's new financial mathematics journal (and will give quite a different audience from Chaos, which is where the shorter paper appears). There will definitely be more of this from my group in the future, as D.Phil. student Dan Fenn (who did the heavy lifting on the paper) continues to produce excellent work and a new D.Phil. student (Martin Gould) will be starting to work on financial networks with my collaborators and me beginning September 1st.

Here is the author list for the short paper: Daniel J. Fenn, Mason A. Porter, Mark McDonald, Stacy Williams, Neil F. Johnson, and Nick S. Jones

Here is the 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.