Authors: Kiriakos Kousoulis, Anthony Rezitis

Title: Crude oil and agricultural commodities dependence structure during Crisis using constant and time-varying copulas.

Abstract

Agricultural and energy markets are consistently interconnected. Crude oil is used as input in agricultural production, while due to the shift to green energy, an increasing demand for commodities used in biofuel production occurred. In the U.S.A., ethanol production is fueled by corn, while the biodiesel market is dependent on soybeans. At the same time, historically, increased bioethanol production has driven corn prices to a high. Moreover, the increased renewable fuel prices and rising oil prices may drive the prices of renewable fuel feedstocks to new all-time heights (Campiche et al., 2007). Additionally, covid19 pandemic and lockdowns led to a decrease in the production and consumption of fossil fuels. On February 24, 2022, a major geopolitical tension between Russia and Ukraine became a full-scale war. The war in Ukraine has many consequences for the global economy. Ukraine and Russia are among the most significant grain exporters, while Russia also exports enormous quantities of Energy, especially to Europe. This war obstructs the production and trade of essential commodities, like grains and Energy, driving the prices to new hikes and leading to food and energy shortages (Baffes and Nagle, 2022). During significant conflicts (wars), the need for risk management strategies and the increased demand for crude oil led to price inflation (Li et al., 2022). The present study aims to compare the correlation of crude oil and selected agricultural commodities during different geopolitical, financial, and health crises eras.

Our research uses daily future contract prices for selected assets (crude oil, wheat, corn, soybeans). We utilize ARMA-GJR-GARCH-Skew t and ARMA-GJR-GARCH-EDF to capture the marginal returns' asymmetry and tail effects. The joint distribution is estimated by constant (i.e., Normal, Student t, Clayton, Rotated Gumbel) and time-varying copula (i.e., Student t GAS, Rotated Gumbel GAS) methods. Furthermore, the optimal copula method for forecasting, Value at Risk, and Expected Shortfall, will be manifested in each turbulent period. Copulas can link different marginal distributions for each chosen asset. Those models can capture the nonlinear dependence and fat tail distribution structure between assets. According to Patton (2013), the dynamic dependence between two financial assets (indices) fails to be captured by the simple static copula models. Moreover, copulas are widely used in portfolio optimization, systemic risk, and risk management (Aas et al., 2009; Ning, 2010; Kalotychou, 2012; Patton, 2013; Schepsmeier, 2015; Low et al., 2016; Low, 2018; Gong et al., 2019).

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