Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/192934
Authors: 
Bettendorf, Timo
Heinlein, Reinhold
Year of Publication: 
2019
Series/Report no.: 
Bundesbank Discussion Paper 06/2019
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.
Subjects: 
connectedness
networks
graph theory
vector autoregression
exchange rates
JEL: 
C32
C51
F31
ISBN: 
978-3-95729-558-3
Document Type: 
Working Paper
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