The relationship between oil and agricultural commodity prices in south africa: a quantile causality approach

Mehmet Balcilar, Shinhye Chang, Rangan Gupta, Vanessa Kasongo and Clement Kyei

Journal of Developing Areas, 2016, vol. 50, issue 2, 137-152

Abstract: The increase in agricultural commodity prices in the recent past has renewed interest in ascertaining the factors that drive agricultural commodity prices. Though a number of factors are possible, higher oil prices are thought to be the major factor driving up agricultural commodity prices, especially as the demand for biofuels production increases. However, empirical evidence of this relationship remain ambiguous and largely depends on the method used. For this reason, there is a need to examine the relationship in the context of different methodologies. Furthermore, information on how South African commodity prices respond to world oil price shocks is less certain. A good understanding of the factors that drive local commodity prices will assist in making sound agricultural policies. In this paper, the Granger causality test is applied to the mean to investigate the causality between oil prices and agricultural (soya beans, wheat, sunflower and corn) commodity prices in South Africa. Daily data spanning from 19 April 2005 to 31 July 2014 is used for Brent crude oil, corn, wheat, sunflower and soya beans prices. Agricultural commodity prices were obtained from the Johannesburg Stock Exchange, and the series of Brent crude oil prices from the U.S. Department of Energy. Results from the linear causality test indicate that oil prices do not influence agricultural commodity prices. However, owing to structural breaks and nonlinear dependence between the variables of study, these results are misleading. As an alternative, the nonparametric test of Granger causality in quantiles, as proposed by Jeong, Härdle and Song (2012) is used. Through this test, we not only look at causality beyond the mean estimates but also accounts for the structural breaks and nonlinear dependence present in the data. Additionally, the method becomes more instructive in the case where the distribution of variables has fat tails. The findings show that the effect of changes in oil prices on agricultural commodity prices vary across the different quantiles of the conditional distribution. The highest impact is not at the median, and the impact on the tails is lower compared to the rest of the distribution. The analysis shows that the relationship between oil prices and agricultural commodity prices depends on specific phases of the market, and therefore contradicts the neutrality hypothesis that oil prices do not cause agricultural commodity prices in South Africa. This implies that policies to stabilize domestic agricultural commodity prices must consider developments in the world oil markets.

Date: 2016
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