Causality between oil shocks and exchange rate: A Bayesian, graph-based VAR approach

Libo Yin () and Xiyuan Ma

Physica A: Statistical Mechanics and its Applications, 2018, vol. 508, issue C, 434-453

Abstract: Our paper studies the casual relationship between oil and major bilateral exchange rates against US dollar via a novel Bayesian, graph-based approach. This approach is shown to be quite effective in dealing with identification in Vector Autoregression (VAR) model, in which the temporal causal structure is represented by a graph sampled by Markov Chain Monte Carlo (MCMC) method. Empirical evidence demonstrates that oil price leads the exchange market in the after-crisis period whereas vice versa before crisis, implying a potential impact from financial crisis on the causality between these two markets. We further show that in general, oil-market specific shock affects the dependence structure most, while aggregate demand shock plays a weaker role and supply shock contributes least. Specifically, these three oil shocks take effect during different periods, thus capturing some invisible information about market evolutions.

Keywords: Oil shocks; Exchange rates; Bayesian; Graph-based VAR; Causal structural relationship; Time-varying analysis (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:508:y:2018:i:c:p:434-453

DOI: 10.1016/j.physa.2018.05.064

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