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Price volatility forecasts for agricultural commodities: an application of volatility models, option implieds and composite approaches forfutures prices of corn and wheat

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  • Guillermo Benavides Perales

    (Banco de México y Tecnológico de Monterrey)

Abstract

There has been substantial research effort aimed to forecast futures price return volatilities of financial and commodity assets. Some part of this research focuses on the performance of time-series models (in particular ARCH models) versus option implied volatility models. A significant part of the literature related to this topic shows that volatility forecast accuracy is not easy to estimate regardless of the forecasting model applied. This paper examines the volatility accuracy of volatility forecast models for the case of corn and wheat futures price returns. The models applied here are a univariate GARCH, a multivariate ARCH (the BEKK model), an option implied and a composite forecast model. The composite model includes time-series (historical) and option implied volatility forecasts. The results show that the option implied model is superior to the historical models in terms of accuracy and that the composite forecast model was the most accurate one (compared to the alternative models) having the lowest mean-square-errors. Given these findings it is recommended to use a composite forecast model if both types of data are available i.e. the time-series (historical) and the option implied. In addition, the results of this paper are consistent to that part of the literature that emphasizes the difficulty on being accurate about forecasting asset price return volatility. This is because the explanatory power (coefficient of determination) calculated in the forecast regressions were relatively low

Suggested Citation

  • Guillermo Benavides Perales, 2009. "Price volatility forecasts for agricultural commodities: an application of volatility models, option implieds and composite approaches forfutures prices of corn and wheat," Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), Tecnológico de Monterrey, Campus Ciudad de México, vol. 3(2), pages 40-59.
    • Handle: RePEc:ega:rafega:200909
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    References listed on IDEAS

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    Cited by:

    1. Guillermo Benavides, 2021. "Asymmetric Volatility Relevance in Risk Management: An Empirical Analysis using Stock Index Futures," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(TNEA), pages 1-18, Septiembr.
    2. Villalba Padilla, Fátima Irina & Flores-Ortega, Miguel, 2014. "Análisis de la volatilidad del índice principal del mercado bursátil mexicano, del índice de riesgo país y de la mezcla mexicana de exportación mediante un modelo GARCH trivariado asimétrico || Volati," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 17(1), pages 3-22, June.
    3. Guillermo Benavides, 2010. "Forecasting Short-Run Inflation Volatility using Futures Prices: An Empirical Analysis from a Value at Risk Perspective," Revista de Administración, Finanzas y Economía (Journal of Management, Finance and Economics), Tecnológico de Monterrey, Campus Ciudad de México, vol. 4(2), pages 1-27.

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    More about this item

    Keywords

    Agricultural commodities; BEKK model; multivariate GARCH; Samuelson hypothesis; theory of storage;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness

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