Connectedness between G10 currencies: Searching for the causal structure

Bettendorf, Timo; Heinlein, Reinhold

Abstract

This paper presents a new approach for modelling the connectedness between asset returns. We adapt the measure of Diebold and Yilmaz, which is based on the forecast error variance decomposition of a VAR model. However, their connectedness measure hinges on critical assumptions with regard to the variance–covariance matrix of the error terms. We propose to use a more agnostic empirical approach, based on a machine learning algorithm, to identify the contemporaneous structure. In a Monte Carlo study, we compare the different connectedness measures and discuss their advantages and disadvantages. In an empirical application we analyse the connectedness between the G10 currencies. Our results suggest that the US dollar as well as the Norwegian krone are the most independent currencies in our sample. By contrast, the Swiss franc and New Zealand dollar have a negligible impact on other currencies. Moreover, a cluster analysis suggests that the currencies can be divided into three groups, which we classify as: commodity currencies, European currencies and safe haven/carry trade financing currencies.

• Journal Article Type: Article
• Acceptance Date: Apr 9, 2022
• Online Publication Date: Apr 28, 2022
• Publication Date: Oct 31, 2023
• Deposit Date: Apr 21, 2022
• Publicly Available Date: Nov 3, 2023
• Journal: International Journal of Finance and Economics
• Print ISSN: 1076-9307
• Electronic ISSN: 1099-1158
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 28
• Issue: 4
• Pages: 3938-3959
• DOI: https://doi.org/10.1002/ijfe.2629
• Keywords: connectedness; exchange rates; graph theory; networks
• Public URL: https://uwe-repository.worktribe.com/output/9378479
• Publisher URL: https://onlinelibrary.wiley.com/doi/10.1002/ijfe.2629