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Modelling the Effects of Oil Prices on Global Fertilizer Prices and Volatility

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  • Chang, C-L.
  • Chen, C-C.
  • McAleer, M.J.
  • Chen, P-Y.

Abstract

The main purpose of this paper is to evaluate the effect of crude oil price on global fertilizer prices in both the mean and volatility. The endogenous structural breakpoint unit root test, ARDL model, and alternative volatility models, including GARCH, EGARCH, and GJR models, are used to investigate the relationship between crude oil price and six global fertilizer prices. The empirical results from ARDL show that most fertilizer prices are significantly affected by the crude oil price while the volatility of global fertilizer prices and crude oil price from March to December 2008 are higher than in other periods.

Suggested Citation

  • Chang, C-L. & Chen, C-C. & McAleer, M.J. & Chen, P-Y., 2013. "Modelling the Effects of Oil Prices on Global Fertilizer Prices and Volatility," Econometric Institute Research Papers EI 2013-04, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:38697
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    Cited by:

    1. Chia-Lin Chang & Yiying Li & Michael McAleer, 2018. "Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice," Energies, MDPI, vol. 11(6), pages 1-19, June.
    2. Khalfaoui, Rabeh & Baumöhl, Eduard & Sarwar, Suleman & Výrost, Tomáš, 2021. "Connectedness between energy and nonenergy commodity markets: Evidence from quantile coherency networks," Resources Policy, Elsevier, vol. 74(C).
    3. David E. Allen & Chialin Chang & Michael McAleer & Abhay K Singh, 2018. "A cointegration analysis of agricultural, energy and bio-fuel spot, and futures prices," Applied Economics, Taylor & Francis Journals, vol. 50(7), pages 804-823, February.
    4. Khalfaoui, Rabeh & Shahzad, Umer & Ghaemi Asl, Mahdi & Ben Jabeur, Sami, 2023. "Investigating the spillovers between energy, food, and agricultural commodity markets: New insights from the quantile coherency approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 63-80.
    5. 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.
    6. Alamah, Zein & Elgammal, Walid & Fakih, Ali, 2024. "Does twitter economic uncertainty matter for wheat prices?," Economics Letters, Elsevier, vol. 234(C).
    7. Andre Yone Haughton & Emma M. Iglesias, 2017. "Exchange Rate Movements, Stock Prices and Volatility in the Caribbean and Latin America," International Journal of Economics and Financial Issues, Econjournals, vol. 7(2), pages 437-447.
    8. Harun Uçak & Yakup Ari & Esin Yelgen, 2022. "The volatility connectedness among fertilisers and agricultural crop prices: Evidence from selected main agricultural products," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 68(9), pages 348-360.
    9. Zhengliang Yang & Xiaoxue Du & Liang Lu & Hernan Tejeda, 2022. "Price and Volatility Transmissions among Natural Gas, Fertilizer, and Corn Markets: A Revisit," JRFM, MDPI, vol. 15(2), pages 1-14, February.

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    More about this item

    JEL classification:

    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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