IDEAS home Printed from https://ideas.repec.org/a/ris/ecoint/0774.html
   My bibliography  Save this article

The Nonparametric Relationship between Oil and South African Agricultural Prices - La relazione nonparametrica tra il prezzo del petrolio e i prezzi dei prodotti agricoli in Sud Africa

Author

Listed:
  • Ajmi, Ahdi N.

    (College of Science and Humanities in Slayel, Salman bin Abdulaziz University)

  • Gupta, Rangan

    (Department of Economics, University of Pretoria)

  • Kruger, Monique

    (Department of Economics, University of Pretoria)

  • Schoeman, Nicola

    (Department of Economics, University of Pretoria)

  • Walters, Leoné

    (Department of Economics, University of Pretoria)

Abstract

The aim of this paper is to investigate the causal relationship between agricultural prices in South Africa and global oil prices. A nonlinear Granger causality test based on moment conditions, introduced by Nishiyama et al. (2011) is employed and we find that there is indeed a causal relationship between global oil prices (OPEC basket (sourced from OPEC) and Brent Crude (sourced from the Fred database of the Federal Reserve Bank of St. Louis)) and certain South African agricultural commodity prices (sourced from Johannesburg Stock Exchange) over the period of 2003-2014 using daily data. The mean price of wheat, sunflower and soya are Granger caused by OPEC basket oil price. OPEC basket oil prices also cause volatility of wheat, sunflower seed and sorghum prices. - Lo scopo di questo studio è analizzare la relazione causale tra il prezzo del petrolio a livello mondiale e i prezzi dei prodotti agricoli in Sud Africa. Viene utilizzato il test di nonlinearità di Granger causality (ideato da Nishiyama et al., 2011) basato su condizioni di momento. I risultati indicano che effettivamente c’è una relazione causale tra i prezzi del petrolio (paniere OPEC), il prezzo Crude Brent (banca dati Federal Reserve Bank of St. Louis) e i prezzi di alcuni prodotti agricoli del Sud Africa (fonte: Borsa di Johannesburg) nel periodo 2003-2014, con utilizzo di dati giornalieri. Il prezzo medio della farina, girasole e soia sono Granger causati dal prezzo del petrolio del paniere OPEC. Quest’ultimo è causa anche della volatilità dei prezzi della farina, dei semi di girasole e del sorgo.

Suggested Citation

  • Ajmi, Ahdi N. & Gupta, Rangan & Kruger, Monique & Schoeman, Nicola & Walters, Leoné, 2016. "The Nonparametric Relationship between Oil and South African Agricultural Prices - La relazione nonparametrica tra il prezzo del petrolio e i prezzi dei prodotti agricoli in Sud Africa," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 69(2), pages 93-112.
  • Handle: RePEc:ris:ecoint:0774
    as

    Download full text from publisher

    File URL: http://www.iei1946.it/upload/rivista_articoli/allegati/102_gupta-non-para100517.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Richard Ajayi & Apostolos Serletis, 2009. "Testing for causality in the transmission of Eurodollar and US interest rates," Applied Financial Economics, Taylor & Francis Journals, vol. 19(6), pages 439-443.
    2. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669.
    3. David P. Vincent & Peter B. Dixon & B.R. Parmenter & D.C. Sams, 1979. "The Short‐Term Effect Of Domestic Oil Price Increases On The Australian Economy With Special Reference To The Agricultural Sector," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 23(2), pages 79-101, August.
    4. Chen, Sheng-Tung & Kuo, Hsiao-I & Chen, Chi-Chung, 2010. "Modeling the relationship between the oil price and global food prices," Applied Energy, Elsevier, vol. 87(8), pages 2517-2525, August.
    5. Nishiyama, Yoshihiko & Hitomi, Kohtaro & Kawasaki, Yoshinori & Jeong, Kiho, 2011. "A consistent nonparametric test for nonlinear causality—Specification in time series regression," Journal of Econometrics, Elsevier, vol. 165(1), pages 112-127.
    6. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    7. Christiane Baumeister & Lutz Kilian, 2014. "Do oil price increases cause higher food prices? [Biofuels, binding constraints, and agricultural commodity price volatility]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 29(80), pages 691-747.
    8. Nazlioglu, Saban & Soytas, Ugur, 2011. "World oil prices and agricultural commodity prices: Evidence from an emerging market," Energy Economics, Elsevier, vol. 33(3), pages 488-496, May.
    9. Gohin, A. & Chantret, F., 2010. "The long-run impact of energy prices on world agricultural markets: The role of macro-economic linkages," Energy Policy, Elsevier, vol. 38(1), pages 333-339, January.
    10. Minot, Nicholas, 2013. "How volatile are African food prices?:," Research briefs 19, International Food Policy Research Institute (IFPRI).
    11. Nazlioglu, Saban, 2011. "World oil and agricultural commodity prices: Evidence from nonlinear causality," Energy Policy, Elsevier, vol. 39(5), pages 2935-2943, May.
    12. Avalos, Fernando, 2014. "Do oil prices drive food prices? The tale of a structural break," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 253-271.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ahdi N. Ajmi & Rangan Gupta & Monique Kruger & Nicola Schoeman & Leoné Walters, 2014. "The Nonparametric Relationship between Oil and South African Agricultural Prices," Working Papers 201461, University of Pretoria, Department of Economics.
    2. Fowowe, Babajide, 2016. "Do oil prices drive agricultural commodity prices? Evidence from South Africa," Energy, Elsevier, vol. 104(C), pages 149-157.
    3. Cheng, Sheng & Cao, Yan, 2019. "On the relation between global food and crude oil prices: An empirical investigation in a nonlinear framework," Energy Economics, Elsevier, vol. 81(C), pages 422-432.
    4. Sun, Yunpeng & Gao, Pengpeng & Raza, Syed Ali & Shah, Nida & Sharif, Arshian, 2023. "The asymmetric effects of oil price shocks on the world food prices: Fresh evidence from quantile-on-quantile regression approach," Energy, Elsevier, vol. 270(C).
    5. Rafiq, Shuddhasattwa & Bloch, Harry, 2016. "Explaining commodity prices through asymmetric oil shocks: Evidence from nonlinear models," Resources Policy, Elsevier, vol. 50(C), pages 34-48.
    6. Raza, Syed Ali & Guesmi, Khaled & Belaid, Fateh & Shah, Nida, 2022. "Time-frequency causality and connectedness between oil price shocks and the world food prices," Research in International Business and Finance, Elsevier, vol. 62(C).
    7. Xu Xiaojie, 2018. "Linear and Nonlinear Causality between Corn Cash and Futures Prices," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 16(2), pages 1-16, November.
    8. Cao, Yan & Cheng, Sheng, 2021. "Impact of COVID-19 outbreak on multi-scale asymmetric spillovers between food and oil prices," Resources Policy, Elsevier, vol. 74(C).
    9. Mourad Zmami & Ousama Ben-Salha, 2019. "Does Oil Price Drive World Food Prices? Evidence from Linear and Nonlinear ARDL Modeling," Economies, MDPI, vol. 7(1), pages 1-18, February.
    10. Duc Hong Vo & Tan Ngoc Vu & Anh The Vo & Michael McAleer, 2019. "Modeling the Relationship between Crude Oil and Agricultural Commodity Prices," Energies, MDPI, vol. 12(7), pages 1-41, April.
    11. Mokni, Khaled & Ben-Salha, Ousama, 2020. "Asymmetric causality in quantiles analysis of the oil-food ‏ ‏nexus since the 1960s," Resources Policy, Elsevier, vol. 69(C).
    12. Kang, Sang Hoon & Tiwari, Aviral Kumar & Albulescu, Claudiu Tiberiu & Yoon, Seong-Min, 2019. "Exploring the time-frequency connectedness and network among crude oil and agriculture commodities V1," Energy Economics, Elsevier, vol. 84(C).
    13. Cagli, Efe Caglar & Taskin, Dilvin & Evrim Mandaci, Pınar, 2019. "The short- and long-run efficiency of energy, precious metals, and base metals markets: Evidence from the exponential smooth transition autoregressive models," Energy Economics, Elsevier, vol. 84(C).
    14. Karakotsios, Achillefs & Katrakilidis, Constantinos & Kroupis, Nikolaos, 2021. "The dynamic linkages between food prices and oil prices. Does asymmetry matter?," The Journal of Economic Asymmetries, Elsevier, vol. 23(C).
    15. Ren, Weijie & Li, Baisong & Han, Min, 2020. "A novel Granger causality method based on HSIC-Lasso for revealing nonlinear relationship between multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    16. Yip, Pick Schen & Brooks, Robert & Do, Hung Xuan & Nguyen, Duc Khuong, 2020. "Dynamic volatility spillover effects between oil and agricultural products," International Review of Financial Analysis, Elsevier, vol. 69(C).
    17. Reboredo, Juan C., 2012. "Do food and oil prices co-move?," Energy Policy, Elsevier, vol. 49(C), pages 456-467.
    18. Bahloul, Walid & Balcilar, Mehmet & Cunado, Juncal & Gupta, Rangan, 2018. "The role of economic and financial uncertainties in predicting commodity futures returns and volatility: Evidence from a nonparametric causality-in-quantiles test," Journal of Multinational Financial Management, Elsevier, vol. 45(C), pages 52-71.
    19. Jinghua Wang & Geoffrey Ngene, 2018. "Symmetric and asymmetric nonlinear causalities between oil prices and the U.S. economic sectors," Review of Quantitative Finance and Accounting, Springer, vol. 51(1), pages 199-218, July.
    20. Palazzi, Rafael Baptista & Meira, Erick & Klotzle, Marcelo Cabus, 2022. "The sugar-ethanol-oil nexus in Brazil: Exploring the pass-through of international commodity prices to national fuel prices," Journal of Commodity Markets, Elsevier, vol. 28(C).

    More about this item

    Keywords

    Agricultural Prices; Oil Prices; Granger Causality; Nonlinearity; South Africa;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • Q10 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - General
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ris:ecoint:0774. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Angela Procopio (email available below). General contact details of provider: https://edirc.repec.org/data/cacogit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.