title: Detecting a currency's dominance or dependence using foreign exchange network trees.
abstract: In a system containing a large number of interacting stochastic processes, there will typically be many nonzero correlation coefficients. This makes it difficult to either visualize the system's interdependencies, or identify its dominant elements. Such a situation arises in foreign exchange (FX), which is the world's biggest market. Here we develop a network analysis of these correlations using minimum spanning trees (MSTs). We show that not only do the MSTs provide a meaningful representation of the global FX dynamics, but they also enable one to determine momentarily dominant and dependent currencies. We find that information about a country's geographical ties emerges from the raw exchange-rate data. Most importantly from a trading perspective, we discuss how to infer which currencies are "in play" during a particular period of time.
authors.affiliation: Mathematics Department, Oxford University, Oxford, OX1 2EL, United Kingdom.
authors.forename: Mark
authors.initials: M
authors.lastname: McDonald
authors.forename: Omer
authors.initials: O
authors.lastname: Suleman
authors.forename: Stacy
authors.initials: S
authors.lastname: Williams
authors.forename: Sam
authors.initials: S
authors.lastname: Howison
authors.forename: Neil F
authors.initials: NF
authors.lastname: Johnson