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

• Published in: Physica A Vol. 508; pp. 434 - 453
• Main Authors: Yin, Libo, Ma, Xiyuan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.10.2018
• Subjects: Bayesian; Causal structural relationship; Exchange rates; Graph-based VAR; Oil shocks; Time-varying analysis; Time-varying analysis; Exchange rates; Graph-based VAR; Causal structural relationship; Oil shocks; Bayesian
• ISSN: 0378-4371; 1873-2119
• DOI: 10.1016/j.physa.2018.05.064

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. •We study the causal structure between oil and major currencies.•We use a novel Bayesian, graph-based approach.•Oil leads exchange markets during the after-crisis period whereas vice versa before crisis.•Three oil shocks play different role during different periods.•In general, oil-market specific shock affects the dependence structure most.•The aggregate demand shock plays a weaker role and supply shock contributes least.