Dynamical Hierarchical Tree in Currency Markets

In this paper we introduce a new method to describe dynamical patterns of the real exchange rate movements time series and to analyze contagion in currency crisis. The method combines the tools of Symbolic Time Series Analysis with the nearest neighbor single linkage clustering algorithm. Data symbolization allows to obtain a metric distance between two different time series that is used to construct an ultrametric distance. By analyzing the data of various countries, we derive a hierarchical organization, constructing minimal-spanning and hierarchical trees. From these trees we detect different clusters of countries according to their proximity. We show that the derived clusters corresponds with the geographical location of the countries. The obtained classification of countries can be used to study the contagion phenomena in currency crisis