Connectedness between G10 Currencies: Searching for the Causal Structure

35 Pages Posted: 25 Feb 2019

See all articles by Timo Bettendorf

Timo Bettendorf

Deutsche Bundesbank

Reinhold Heinlein

Keele University - Keele Management School

Date Written: 2019

Abstract

This paper presents a new approach for modelling the connectedness between asset returns. We adapt the measure of Diebold and Y¸lmaz (2014), 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.

Keywords: connectedness, networks, graph theory, vector autoregression, exchange rates

JEL Classification: C32, C51, F31

Suggested Citation

Bettendorf, Timo and Heinlein, Reinhold, Connectedness between G10 Currencies: Searching for the Causal Structure (2019). Deutsche Bundesbank Discussion Paper No. 06/2019, Available at SSRN: https://ssrn.com/abstract=3337431

Timo Bettendorf (Contact Author)

Deutsche Bundesbank ( email )

Wilhelm-Epstein-Str. 14
Frankfurt/Main, 60431
Germany

Reinhold Heinlein

Keele University - Keele Management School ( email )

Darwin Building
Staffordshire, ST5 5BG
United Kingdom

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