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In this paper we study data from financial markets using aninformation-theory tool that we call the normalised Mutual Information Rate andshow how to use it to infer the underlying network structure of interrelationsin foreign currency exchange rates and stock indices of 14 countries world-wideand the European Union. We first present the mathematical method and discussabout its computational aspects, and then apply it to artificial data fromchaotic dynamics and to correlated random variates. Next, we apply the methodto infer the network structure of the financial data. Particularly, we studyand reveal the interrelations among the various foreign currency exchange ratesand stock indices in two separate networks for which we also perform ananalysis to identify their structural properties. Our results show that bothare small-world networks sharing similar properties but also having distinctdifferences in terms of assortativity. Finally, the consistent relationshipsdepicted among the 15 economies are further supported by a discussion from theeconomics view point.

Inference of forex and stock-index financial networks based on the normalised mutual information rate