Arbeitspapier

Connectedness between G10 currencies: Searching for the causal structure

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.

ISBN
978-3-95729-558-3
Sprache
Englisch

Erschienen in
Series: Deutsche Bundesbank Discussion Paper ; No. 06/2019

Klassifikation
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Model Construction and Estimation
Foreign Exchange
Thema
connectedness
networks
graph theory
vector autoregression
exchange rates

Ereignis
Geistige Schöpfung
(wer)
Bettendorf, Timo
Heinlein, Reinhold
Ereignis
Veröffentlichung
(wer)
Deutsche Bundesbank
(wo)
Frankfurt a. M.
(wann)
2019

Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Bettendorf, Timo
  • Heinlein, Reinhold
  • Deutsche Bundesbank

Entstanden

  • 2019

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