Detecting a Currency's Dominance or Dependence using Foreign Exchange Network Trees

TL;DR

This paper introduces a network analysis method using Minimum Spanning Trees to visualize and identify dominant or dependent currencies in the global foreign exchange market, revealing geographical and temporal dynamics.

Contribution

It develops a novel application of MSTs to FX data, enabling real-time detection of currency dominance and dependence, and uncovers geographical influences in currency interactions.

Findings

MSTs effectively represent global FX dynamics.

The method identifies momentarily dominant currencies.

Geographical ties influence currency relationships.

Abstract

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.