In this paper we investigate the dynamic relations between crude oil price returns and a set of energy price returns, namely diesel, gasoline, heating, and the natural gas. This is performed by means of Granger non-causality tests for US spot closing prices over the period from January 1997 to December 2017. In previous studies this has been done by testing for the added predictive value of including lagged values of one energy price return in predicting the conditional expectation of another. In this paper, we instead focus on different ranges of the full conditional distribution within the framework of a dynamic quantile regression model, and identify the quantile ranges from which causality arises. The results constitute a richer set of findings than what is possible by just considering a single moment of the conditional distribution, which can be useful for implementing better substitution investment strategies and effective policy interventions. We find several interesting one-directional dynamic relations between the employed energy prices, especially in the tail quantiles, but also a bi-directional causal relation between energy prices for which the classical Granger non-causality test suggests otherwise. Our results are robust to alternative measures of the price of oil and different data frequencies.nonPeerReviewe
