The COVID-19 Pandemic and Agricultural Futures in the USA: Evidence From a Dynamic Fourier Quantile Causality Test

The COVID-19 Pandemic and Agricultural Futures in the USA: Evidence From a Dynamic Fourier Quantile Causality Test

Ugur Korkut Pata, Onder Ozgur, Veli Yilanci, Muhammed Sehid Gorus
DOI: 10.4018/978-1-7998-9648-7.ch015
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This study aims to examine the impact of the COVID-19 pandemic on various agricultural commodity futures (cocoa, coffee, corn, cotton, soybean meal, soybeans, sugar, and wheat) in the United States for the period from January 24, 2020, to July 6, 2021, considering oil prices as a control variable. Specifically, the study employs a novel Fourier quantile causality test and its time-varying form. The results show that the causal relationships between COVID-19 cases and agricultural commodity futures are highly time-varying. The empirical findings also demonstrate that COVID-19 has the strongest causal effect on coffee futures, followed by sugar, soybeans, and corn. In contrast, the impact of COVID-19 on cocoa and cotton futures is relatively limited. The causal effect of COVID-19 on agricultural futures is more pronounced at lower quantiles and in the spring and summer months. In general, COVID-19 has significant predictive power for the six agricultural commodity futures over 100 days in the analysis period, with the exception of cocoa and cotton.
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The global economy was hit by the COVID-19 pandemic in late 2019. Most governments have adopted stringent economic and social policies to prevent the spread of COVID-19, including restrictions on mass gatherings, internal and/or international travel restrictions, school closures, etc. (Cheng et al. 2020). Although these measures helped to slow the virus from spreading, they had a significant economic impact. According to Aggarwal et al. (2021), the stringent lockdown measures led to difficulties in conducting day-to-day business. As a result, most industries, including agriculture, were severely impacted by the virus' effects.

COVID-19 has harmed the agricultural market by disrupting agricultural commodity demand and supply (Elleby et al. 2020). COVID-19 has a greater impact on demand for agricultural products than on supply due to its limited accessibility (Siche et al. 2020). On the one hand, due to the closure of schools, workplaces, hotels, and restaurants, demand for agricultural commodities has decreased. The demand for agricultural goods has decreased as the unemployment rate has risen and per capita income has decreased (Varshney et al. 2020). Rising food costs and income losses due to COVID -19 threaten food access and food security in developed and developing countries, with long-term implications (FAO, 2020). In addition, trade restrictions on agricultural goods (Casey and Cimino-Isaacs, 2020) and labor and logistical restrictions (Varshney et al. 2020) have worsened agricultural supply chains. On the other hand, the agricultural sector is supported by the increasing demand for food from consumers who need to eat at home and fill their pantries. Governments around the world have taken action to protect the viability of the food supply chain by attaching importance to the need for food in difficult pandemic conditions (Gray, 2020).

The combined effects of demand and supply disturbances increase food prices in the short-run, and the theoretical implications of COVID-19 on food inflation are widely recognized (Akter, 2020). Similarly, COVID-19 is highly likely to affect agricultural futures prices. In this context, this study aims to examine the impacts of the COVID-19 outbreak on the eight main agricultural futures prices (cocoa, coffee, corn, cotton, soybean meal, soybeans, sugar, and wheat) for the United States from January 24, 2020, to July 6, 2021. This study utilizes the COVID-19 cases (million people) in the United States as an indicator of pandemic severity. In addition, this study adds crude oil prices as a control variable in the model. Crude oil prices have a key role in agricultural price modeling. According to Wang et al. (2020), oil products are used for the production, transportation, and trade of agricultural commodities. Therefore, an increase in crude oil prices affects agricultural commodity prices through the change in demand and/or supply.

During the period under study, agricultural commodity futures prices have been very volatile. Prices for corn, coffee, and cocoa, in particular, fluctuated dramatically (see Figure A.1.). Besides, COVID-19 cases increased multifold. Although the number of cases did not reach triple digits by the end of February 2020, it exceeded 33 million by the end of June 2021. Besides, crude oil prices were negatively affected by the pandemic in the early stages of the outbreak. On June 22, 2020, crude oil prices were $56.76 per barrel, compared to the minus $36.98 per barrel on April 20, 2020. After this date, crude oil prices started to increase gradually. These trends in agricultural commodity prices, crude oil prices, and the number of COVID-19 cases led us to study the causal linkage between these variables in a time-varying environment for the United States.

Key Terms in this Chapter

Futures: It is a standardized legal agreement to buy or sell something at a predetermined price at a certain time in the future. The buyer must purchase, or the seller must sell the underlying asset at the set price.

COVID-19 Pandemic: It is an ongoing global pandemic of coronavirus disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Fourier Approximation: It is a method that considers the gradual structural (instead of sharp breaks) shifts in the series.

Agricultural Commodities: They are crops produced on farms or plantations such as corn, cotton, sugar, and wheat.

Granger Causality: It is a statistical hypothesis test that is developed for determining whether one series is useful in forecasting another or not.

Public Restrictions: It covers restrictions on mass gatherings, internal and international travel restrictions, and school closures to slow down the epidemic.

Quantile: A quantile is where a sample is divided into equal-sized, adjacent, subgroups.

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