  In this paper we study data from financial markets using an
information-theory tool that we call the normalised Mutual Information Rate and
show how to use it to infer the underlying network structure of interrelations
in foreign currency exchange rates and stock indices of 14 countries world-wide
and the European Union. We first present the mathematical method and discuss
about its computational aspects, and then apply it to artificial data from
chaotic dynamics and to correlated random variates. Next, we apply the method
to infer the network structure of the financial data. Particularly, we study
and reveal the interrelations among the various foreign currency exchange rates
and stock indices in two separate networks for which we also perform an
analysis to identify their structural properties. Our results show that both
are small-world networks sharing similar properties but also having distinct
differences in terms of assortativity. Finally, the consistent relationships
depicted among the 15 economies are further supported by a discussion from the
economics view point.
