Skip to content
This repository has been archived by the owner on Dec 10, 2020. It is now read-only.

A clustering exercise of global currencies on three common financial market features using data from 2017 through 2019, as published in Towards Data Science on Medium.com

Notifications You must be signed in to change notification settings

at-tan/Hierarchical_Clustering_of_Currencies

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

11 Commits
 
 
 
 

Repository files navigation

An agglomerative hierarchical clustering exercise on 31 global currencies, including gold and silver, on three common financial market features using data from 2017 through 2019. The final results of four clusters show that geography is of little use in segmenting global currencies in terms of their behavior, except perhaps for non-JPY Asian currencies. The beta to broad US dollar direction offered the best differencing factor across the four clusters, followed by implied volatility, and lastly by equity market correlation. However, one FX cluster ("safe haven cluster") was distinguished by having the only positive equity correlation values.

This is the Python code used for the article published in: https://towardsdatascience.com/a-hierarchical-clustering-of-currencies-80b8ba6c9ff3

About

A clustering exercise of global currencies on three common financial market features using data from 2017 through 2019, as published in Towards Data Science on Medium.com

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published