Topology of Currencies: Persistent Homology for FX Co-movements: A Comparative Clustering Study

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Hauptverfasser: de Jeneret, Pattravadee de Favereau, Diamantis, Ioannis
Format: Preprint
Veröffentlicht: 2025
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author de Jeneret, Pattravadee de Favereau
Diamantis, Ioannis
author_facet de Jeneret, Pattravadee de Favereau
Diamantis, Ioannis
contents This study investigates whether Topological Data Analysis (TDA) can provide additional insights beyond traditional statistical methods in clustering currency behaviours. We focus on the foreign exchange (FX) market, which is a complex system often exhibiting non-linear and high-dimensional dynamics that classical techniques may not fully capture. We compare clustering results based on TDA-derived features versus classical statistical features using monthly logarithmic returns of 13 major currency exchange rates (all against the euro). Two widely-used clustering algorithms, \(k\)-means and Hierarchical clustering, are applied on both types of features, and cluster quality is evaluated via the Silhouette score and the Calinski-Harabasz index. Our findings show that TDA-based feature clustering produces more compact and well-separated clusters than clustering on traditional statistical features, particularly achieving substantially higher Calinski-Harabasz scores. However, all clustering approaches yield modest Silhouette scores, underscoring the inherent difficulty of grouping FX time series. The differing cluster compositions under TDA vs. classical features suggest that TDA captures structural patterns in currency co-movements that conventional methods might overlook. These results highlight TDA as a valuable complementary tool for analysing financial time series, with potential applications in risk management where understanding structural co-movements is crucial.
format Preprint
id arxiv_https___arxiv_org_abs_2510_19306
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Topology of Currencies: Persistent Homology for FX Co-movements: A Comparative Clustering Study
de Jeneret, Pattravadee de Favereau
Diamantis, Ioannis
Machine Learning
General Economics
Economics
Applications
55N31, 62H30, 91B84, 91G70, 68T09
G.2.3; I.5.3; I.5.4
This study investigates whether Topological Data Analysis (TDA) can provide additional insights beyond traditional statistical methods in clustering currency behaviours. We focus on the foreign exchange (FX) market, which is a complex system often exhibiting non-linear and high-dimensional dynamics that classical techniques may not fully capture. We compare clustering results based on TDA-derived features versus classical statistical features using monthly logarithmic returns of 13 major currency exchange rates (all against the euro). Two widely-used clustering algorithms, \(k\)-means and Hierarchical clustering, are applied on both types of features, and cluster quality is evaluated via the Silhouette score and the Calinski-Harabasz index. Our findings show that TDA-based feature clustering produces more compact and well-separated clusters than clustering on traditional statistical features, particularly achieving substantially higher Calinski-Harabasz scores. However, all clustering approaches yield modest Silhouette scores, underscoring the inherent difficulty of grouping FX time series. The differing cluster compositions under TDA vs. classical features suggest that TDA captures structural patterns in currency co-movements that conventional methods might overlook. These results highlight TDA as a valuable complementary tool for analysing financial time series, with potential applications in risk management where understanding structural co-movements is crucial.
title Topology of Currencies: Persistent Homology for FX Co-movements: A Comparative Clustering Study
topic Machine Learning
General Economics
Economics
Applications
55N31, 62H30, 91B84, 91G70, 68T09
G.2.3; I.5.3; I.5.4
url https://arxiv.org/abs/2510.19306