The threat of oil market turmoils to food price stability in Sub-Saharan Africa

Dalheimer, Bernhard; Herwartz, Helmut; Lange, Alexander

doi:10.1016/j.eneco.2020.105029

Cited by 34 publications

(16 citation statements)

References 72 publications

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“…Mensi et al (2014) showed a significant linkage between these energies (WTI oil, Europe Brent oil, gasoline, and heating oil) and cereal markets (barley, corn, sorghum, and wheat). Dalheimer et al (2021) showed that disruptions in global oil supply led to an increase in food prices in African corn markets. The growth in biofuel production has caused a very modest decrease in fossil fuel prices, implying a small fuel rebound effect (Khanna et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

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Dynamic correlation between crude oil and agricultural futures markets

Chen

Yan

Kang

2022

Review Development Economics

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In recent years, the relationship between agricultural commodities and crude oil has become increasingly close with the promotion of biofuel policies. This study examines the dynamic correlation between global crude oil futures and seven agricultural commodity futures by applying the consistent dynamic conditional correlation and dynamic equicorrelation models. The empirical results show that the dynamic correlation between the global crude oil futures market and China's agricultural futures market is weak compared to the global agricultural futures market. In particular, soybean oil has the strongest correlation with crude oil, while Dalian Commodity Exchange (DCE) corn and Zhengzhou Commodity Exchange wheat have the weakest correlation with crude oil. There is an indirect linkage between crude oil futures and DCE soybean meal and DCE soybean oil. Moreover, the dynamic correlation between crude oil and agricultural commodities increased during the financial crisis, the novel coronavirus (COVID‐19) epidemic, and the crude oil crash crisis. Brent crude oil has a stronger co‐movement with China's agricultural commodities than West Texas Intermediate crude oil and can better hedge the risk of agricultural commodities. The findings of this study provide some insights into the contagion risk management of crude oil futures and agricultural futures markets.

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Section: Literature Reviewmentioning

confidence: 99%

“…Dalheimer et al. (2021) showed that disruptions in global oil supply led to an increase in food prices in African corn markets. The growth in biofuel production has caused a very modest decrease in fossil fuel prices, implying a small fuel rebound effect (Khanna et al., 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic correlation between crude oil and agricultural futures markets

Chen

Yan

Kang

2022

Review Development Economics

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“…As important strategic resources, food and oil have been widely studied by scholars (Dalheimer et al, 2021;Mokni and Ben-Salha, 2020;Sarwar et al, 2020). In recent years, international food prices have continued to rise.…”

Section: Introductionmentioning

confidence: 99%

“…Many reasons contribute to rising prices of food; however, high oil prices are a major factor. The expansion of biomass energy-such as biodiesel and bioethanol-in response to climate change has caused a crowding-out effect on food production for people, while high oil prices have increased agricultural costs incurred in the production and transportation processes (Gardebroek and Hernandez, 2013;Sarwar et al, 2020;Dalheimer et al, 2021). Rising food prices have posed a substantial threat to food security especially for countries that rely heavily on importing food.…”

Section: Introductionmentioning

confidence: 99%

Modelling the symmetrical and asymmetrical effects of global oil prices on local food prices: A MENA region application

cherif

Chen

2021

Environ Sci Pollut Res

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This paper explores the complex nexus between the global oil prices and the food prices of Middle East and North Africa (MENA) region during the period 2000-2020. Both linear and nonlinear models of the autoregressive distributed lag (ARDL) approach are adapted into panel data form to investigate the symmetrical and asymmetrical influence of oil prices on food prices. The key results are summarized: i) The effect of oil prices on food prices is significantly positive including both oil-exporting and oil-importing nations are verified in the long-term. The positive impact on oil-exporters-due to higher oil revenues-is greater than importing nations, leading to an increased demand for food. Additionally, the effect on oilexporters is negative and significant in the short-term but not significant for importers. ii) The panel analysis for the MENA sample confirms the presence of negative short-term asymmetric behaviour, while in the long-term, the asymmetric effect is positive, indicating that food prices increase regardless of fluctuations in oil prices. iii) Wald test results support asymmetric cointegration for the whole sample of the MENA due to the heterogeneous response within the oil-importing and exporting samples. Specifically, the non-linear ARDL test results affirm the absence of an asymmetric nexus among oil and food prices for oil-exporting group (including Saudi Arabia, Saudi Arabia, United Arab Emirates) and Tunisia within the oil-importing group.Although there are differences in the direction and degree, the food prices of other countries

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“…A large number of different time series techniques have already been used for the investigation of biofuel-related price transmission analyses. Some of them are very standard mainstream time series econometrics techniques, such as the vector error correction model (Zhang et al 2010;Ciaian and Kancs 2011a, b;Rajčaniová and Pokrivčák 2011), vector autoregression or structural vector autoregression (Capitani et al 2018;Dalheimer et al 2021), generalised autoregressive conditional heteroscedasticity models (Abdelradi and Serra 2015), autoregressive distributed lag (Dutta 2018) and more general Granger causality approaches (Bastianin et al 2016). However, less common techniques, such as copulas (Reboredo 2012;Tiwari et al 2021), wavelets (Pal and Mitra 2017) and frequency-dependent spillovers (Pal and Mitra 2020), are used as well.…”

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confidence: 99%

Price transmission in biofuel-related global agricultural networks

Janda¹,

Krištoufek²,

Schererová³

et al. 2021

AGR ECON-CZECH

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This article investigates the connections among the prices of biofuels, agricultural commodities and other relevant assets in Europe, the US, and Brazil. The analysis includes a comprehensive data set covering price data for 38 traded titles during the period from 2003 to 2020. We used the minimum spanning tree (MST) approach to identify price connections in a complex trading system. Our analysis of mutual price connections reveals the major defining features of world-leading biofuel markets. We provide the characteristics of the main bioethanol and biodiesel markets with respect to government policies and technical and local features of the production and consumption of particular biofuels. Despite a relatively long and dynamically evolving history of biofuels, the biofuel systems in the US, Brazil and Europe do not converge toward the same pattern of relations among fossil fuels, biofuels, agricultural commodities and financial assets.

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The threat of oil market turmoils to food price stability in Sub-Saharan Africa

Cited by 34 publications

References 72 publications

Dynamic correlation between crude oil and agricultural futures markets

Dynamic correlation between crude oil and agricultural futures markets

Modelling the symmetrical and asymmetrical effects of global oil prices on local food prices: A MENA region application

Price transmission in biofuel-related global agricultural networks

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