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Agricultural Commodity Price Volatility and Its Macroeconomic Determinants: A GARCH-MIDAS Approach

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Abstract

This paper investigates the main drivers of the agricultural commodity price volatility using the GARCH-MIDAS model of Engel et al. (2013), a new class of component models that allows for isolating the low-frequency component of volatility and taking into consideration macroeconomic factors via mixed data sampling. We show that modelling the agricultural price volatility as the product of high and low frequency components is more efficient than filtering it through a standard GARCH(1,1) model. After combing wheat, corn and soybean daily prices with monthly market-specific and common macroeconomic drivers over the period 1986-2012, it appears that supply-demand indicators and conventional speculation proxies are crucial in explaining the low-frequency component of volatility while monetary factors and energy markets play significant but less important role. Nevertheless, when we consider only the period following the recent price spikes (2006-2012), the monetary factors especially interest rate become essential to describe agricultural price fluctuations, suggesting also that the heterogeneity in the effects of the drivers on different crops is decreasing.

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  • Emiliano Magrini & Ayca Donmez, 2013. "Agricultural Commodity Price Volatility and Its Macroeconomic Determinants: A GARCH-MIDAS Approach," JRC Research Reports JRC84138, Joint Research Centre.
    • Handle: RePEc:ipt:iptwpa:jrc84138
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    2. Fang, Libing & Yu, Honghai & Xiao, Wen, 2018. "Forecasting gold futures market volatility using macroeconomic variables in the United States," Economic Modelling, Elsevier, vol. 72(C), pages 249-259.
    3. Ruobing Liu & Jianhui Yang & Chuan-Yang Ruan, 2019. "The Impact of Macroeconomic News on Chinese Futures," IJFS, MDPI, vol. 7(4), pages 1-14, October.
    4. Duc Khuong Nguyen & Thomas Walther, 2020. "Modeling and forecasting commodity market volatility with long‐term economic and financial variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 126-142, March.
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    6. Mo, Di & Gupta, Rakesh & Li, Bin & Singh, Tarlok, 2018. "The macroeconomic determinants of commodity futures volatility: Evidence from Chinese and Indian markets," Economic Modelling, Elsevier, vol. 70(C), pages 543-560.
    7. Ayca Donmez & Gloria Solano-Hermosilla & Vladimir Bougay & Balaji Subbaraman & Robert M'barek & Abdoulaye Adam & Stephen Bahemuka & Oliver J. M. Chinganya & Vladimir Eskin & Koua Louis Kouakou & Charl, 2017. "Using web and mobile phone technologies to collect food market prices in Africa. Approaching real-time data and use of crowdsourcing, 2013 - 2016," JRC Research Reports JRC104311, Joint Research Centre.
    8. Aktham Maghyereh & Hussein Abdoh, 2022. "Can news-based economic sentiment predict bubbles in precious metal markets?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-29, December.
    9. Suranjana Joarder, 2018. "The Commodity Futures Volatility and Macroeconomic Fundamentals - The Case of Oil and Oilseed Commodities in India," International Econometric Review (IER), Econometric Research Association, vol. 10(2), pages 33-50, September.

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    More about this item

    Keywords

    Price Volatility; Agricultural Commodities; GARCH-MIDAS; macroeconomic indicators; speculation;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices
    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance

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