Detecting a Currency's Dominance or Dependence using Foreign Exchange   Network Trees
  In a system containing a large number of interacting stochastic processes, there will typically be many non-zero correlation coefficients. This makes it difficult to either visualize the system's inter-dependencies, 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. 
Mark McDonald, Omer Suleman, Stacy Williams, Sam Howison and Neil F.   Johnson (Oxford University and HSBC Bank)