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Forecasting Agricultural Commodity Prices with Asymmetric-Error GARCH Models

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  • Ramirez, Octavio A.
  • Fadiga, Mohamadou L.

Abstract

The performance of a proposed asymmetric-error GARCH model is evaluated in comparison to the normal-error- and Student-t-GARCH models through three applications involving forecasts of U.S. soybean, sorghum, and wheat prices. The applications illustrate the relative advantages of the proposed model specification when the error term is asymmetrically distributed, and provide improved probabilistic forecasts for the prices of these commodities.

Suggested Citation

  • Ramirez, Octavio A. & Fadiga, Mohamadou L., 2003. "Forecasting Agricultural Commodity Prices with Asymmetric-Error GARCH Models," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 28(1), pages 1-15, April.
    • Handle: RePEc:ags:jlaare:30714
    DOI: 10.22004/ag.econ.30714
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    Cited by:

    1. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    2. Crespo-Cuaresma, Jesus & Fortin, Ines & Hlouskova, Jaroslava & Obersteiner, Michael, 2021. "Regime-dependent commodity price dynamics: A predictive analysis," IHS Working Paper Series 28, Institute for Advanced Studies.
    3. Benz, Eva & Trück, Stefan, 2009. "Modeling the price dynamics of CO2 emission allowances," Energy Economics, Elsevier, vol. 31(1), pages 4-15, January.
    4. Wetzstein, Brian & Florax, Raymond & Foster, Kenneth & Binkley, James, 2021. "Transportation costs: Mississippi River barge rates," Journal of Commodity Markets, Elsevier, vol. 21(C).
    5. Jean Pierre Fernández Prada Saucedo & Gabriel Rodríguez, 2020. "Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models," Documentos de Trabajo / Working Papers 2020-484, Departamento de Economía - Pontificia Universidad Católica del Perú.
    6. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    7. Bekkerman, Anton & Pelletier, Denis, 2009. "Basis Volatilities of Corn and Soybean in Spatially Separated Markets: The Effect of Ethanol Demand," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49281, Agricultural and Applied Economics Association.
    8. Ramirez, Octavio A. & Mohanty, Samarendu & Carpio, Carlos E. & Denning, Megan, 2004. "Issues and Strategies for Aggregate Supply Response Estimation for Policy Analyses," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 36(2), pages 1-17, August.
    9. Fathi Abid & Bilel Kaffel, 2018. "The extent of virgin olive-oil prices’ distribution revealing the behavior of market speculators," Review of Quantitative Finance and Accounting, Springer, vol. 50(2), pages 561-590, February.
    10. Amer Ait Sidhoum & Teresa Serra, 2016. "Volatility Spillovers in the Spanish Food Marketing Chain: The Case of Tomato," Agribusiness, John Wiley & Sons, Ltd., vol. 32(1), pages 45-63, January.
    11. Jesus Crespo Cuaresma & Jaroslava Hlouskova & Michael Obersteiner, 2021. "Agricultural commodity price dynamics and their determinants: A comprehensive econometric approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1245-1273, November.
    12. Tiago Silveira Gontijo & Alexandre de C ssio Rodrigues & Cristiana Fernandes De Muylder & Jefferson Lopes la Falce & Thiago Henrique Martins Pereira, 2020. "Analysis of Olive Oil Market Volatility using the ARCH and GARCH techniques," International Journal of Energy Economics and Policy, Econjournals, vol. 10(3), pages 423-428.
    13. Tao XIONG & Chongguang LI & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    14. Kyriazi, Foteini & Thomakos, Dimitrios D. & Guerard, John B., 2019. "Adaptive learning forecasting, with applications in forecasting agricultural prices," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1356-1369.

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