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Modeling the Effect of Oil Price on Global Fertilizer Prices

Author

Listed:
  • Ping-Yu Chen

    (Department of Applied Economics, National Chung Hsing University)

  • Chia-Lin Chang

    (Department of Applied Economics, National Chung Hsing University)

  • Chi-Chung Chen

    (Department of Applied Economics, National Chung Hsing University)

  • Michael McAleer

    (Erasmus University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University)

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, the autoregressive distributed lag (ARDL) model, and alternative volatility models, including the generalized autoregressive conditional heteroskedasticity (GARCH) model, Exponential GARCH (EGARCH) model, and GJR model, are used to investigate the relationship between crude oil price and six global fertilizer prices. Weekly data for 2003-2008 for the seven price series are analyzed. The empirical results from ARDL show that most fertilizer prices are significantly affected by the crude oil price, which explains why global fertilizer prices reached a peak in 2008. We also find that that the volatility of global fertilizer prices and crude oil price from March to December 2008 are higher than in other periods, and that the peak crude oil price caused greater volatility in the crude oil price and global fertilizer prices. As volatility invokes financial risk, the relationship between oil price and global fertilizer prices and their associated volatility is important for public policy relating to the development of optimal energy use, global agricultural production, and financial integration.

Suggested Citation

  • Ping-Yu Chen & Chia-Lin Chang & Chi-Chung Chen & Michael McAleer, 2010. "Modeling the Effect of Oil Price on Global Fertilizer Prices," KIER Working Papers 722, Kyoto University, Institute of Economic Research.
    • Handle: RePEc:kyo:wpaper:722
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    Cited by:

    1. Karel Janda & Ladislav Krištoufek, 2019. "The Relationship Between Fuel and Food Prices: Methods and Outcomes," Annual Review of Resource Economics, Annual Reviews, vol. 11(1), pages 195-216, October.
    2. Adämmer, Philipp & Bohl, Martin T., 2015. "Speculative bubbles in agricultural prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 55(C), pages 67-76.
    3. Eleni Zafeiriou & Garyfallos Arabatzis & Paraskevi Karanikola & Stilianos Tampakis & Stavros Tsiantikoudis, 2018. "Agricultural Commodities and Crude Oil Prices: An Empirical Investigation of Their Relationship," Sustainability, MDPI, vol. 10(4), pages 1-11, April.
    4. da Silveira, Rodrigo Lanna F. & Mattos, Fabio L., 2015. "Price And Volatility Transmission In Livestock And Grain Markets: Examining The Effect Of Increasing Ethanol Production Across Countries," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205684, Agricultural and Applied Economics Association.
    5. Chen, Kuan-Ju & Marsh, Thomas L., "undated". "The Relationship between Biomaterial and Agricultural Commodity Markets," 2018 Annual Meeting, August 5-7, Washington, D.C. 274111, Agricultural and Applied Economics Association.

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    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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