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The COVID-19 effects on agricultural commodity markets

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  • Balcilar, Mehmet
  • Sertoglu, Kamil
  • Agan, Busra

Abstract

This study examines the effect of the COVID-19 pandemic on major agricultural commodity prices (cattle, cocoa, coffee, corn, cotton, hog, rice, soya oil, soybeans, soybean meal, sugar and wheat) using daily data from 1 January 2016 to 25 February 2022. We measured COVID-19 effect using a news-based sentiment index. A robust nonparametric Granger causality-in-quantiles test is used to test the effect of the COVID-19 sentiment on agricultural commodity prices and price volatility. We find significant Granger causality from the news-based COVID-19 sentiment to mean of the agricultural commodity prices in the lower and upper ranges of the quantiles. Moreover, findings show that the COVID-19 sentiment is also causal for variance of agricultural commodity prices, but only above the quantile ranges above the first quarter. Thus, COVID-19 is causal for large volatility changes in agricultural commodity prices. Accordingly, the extremely negative sentiment associated with COVID-19 has not only caused a price crash in agricultural markets, but also significantly increased market risk. Policymakers should be wary of the risks and vulnerabilities of agricultural commodities to extreme events, as well as the ramifications for producers and consumers throughout the economy.

Suggested Citation

  • Balcilar, Mehmet & Sertoglu, Kamil & Agan, Busra, 2022. "The COVID-19 effects on agricultural commodity markets," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 61(3), July.
    • Handle: RePEc:ags:agreko:348167
    DOI: 10.22004/ag.econ.348167
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    1. Abhishek Yadav, 2024. "A Comparative Study of Time Series, Machine Learning, and Deep Learning Models for Forecasting Global Price of Wheat," SN Operations Research Forum, Springer, vol. 5(4), pages 1-24, December.

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