Dynamical Clustering of Exchange Rates

TL;DR

This paper applies network science to analyze the dynamic community structure of the foreign exchange market from 1991 to 2008, revealing how exchange rates' roles and market structure evolve over time.

Contribution

It introduces a novel node-centric community analysis method to track the dynamic roles of exchange rates and identify structural changes in the FX market.

Findings

Identifies dominant exchange rates at different times.

Detects significant shifts in market structure.

Links exchange rate roles to their community positions.

Abstract

We use techniques from network science to study correlations in the foreign exchange (FX) market over the period 1991--2008. We consider an FX market network in which each node represents an exchange rate and each weighted edge represents a time-dependent correlation between the rates. To provide insights into the clustering of the exchange rate time series, we investigate dynamic communities in the network. We show that there is a relationship between an exchange rate's functional role within the market and its position within its community and use a node-centric community analysis to track the time dynamics of this role. This reveals which exchange rates dominate the market at particular times and also identifies exchange rates that experienced significant changes in market role. We also use the community dynamics to uncover major structural changes that occurred in the FX market. Our techniques are general and will be similarly useful for investigating correlations in other markets.