COVID-19, the Russia-Ukraine war and the connectedness between the U.S. and Chinese agricultural futures markets

Globalization and increased international trade have intensified the interconnectedness of global commodity markets. External shocks can amplify risk spillover, as seen during the 2008 financial crisis and the COVID-19 pandemic. Understanding these interconnections is crucial for regulators to implement effective market supervision and for investors to develop successful hedging and investment strategies. While previous research has examined market linkages in specific instances, there's a gap in understanding the interconnectedness of international commodity markets, especially during recent global crises like the COVID-19 pandemic and the Russia-Ukraine war. This study focuses on the impact of these events on the U.S. and Chinese agricultural futures markets, given the significant role of agriculture in both economies. The U.S. is a major agricultural producer and exporter, while China is the largest consumer and importer. The COVID-19 pandemic disrupted agricultural production and supply chains globally, while the Russia-Ukraine war severely impacted agricultural exports from a key region, influencing global food prices. Contagion theory, emphasizing the spread of market shocks through information flows and financial linkages, provides a framework for understanding these interconnections. The rapid dissemination of information and the participation of international actors in futures markets amplify the transmission of shocks. This paper analyzes three major agricultural commodities (soybean, corn, and cotton) traded in both markets to determine the influence of these crises on market linkages.

Existing research has explored the impact of external events on market relationships between developed and emerging economies, but lacks a comprehensive examination of international commodity market connections during recent crises. Studies have analyzed specific commodity pairs, such as US and Canadian wheat or London and Shanghai copper, but not during the context of recent global crises. Recent studies have begun exploring the effects of COVID-19 on the US and Chinese stock and oil futures markets and the differing effects of COVID-19 and the Russia-Ukraine war on US and Chinese stock markets, but further research is needed on the impacts on agricultural commodity markets.

The study utilized daily NYMEX (US) and DFE (China) closing prices for soybean, corn, and cotton futures from March 26, 2018, to November 28, 2023. The Johansen cointegration test was applied to determine long-term associations between US and Chinese agricultural futures returns. Wavelet coherence analysis (WCA) was employed to examine comovements and lead-lag relationships at various time scales. Time-varying parameter vector autoregression (TVP-VAR) was used to investigate the impact of COVID-19 and the Russia-Ukraine war on dynamic volatility spillovers. Finally, VAR models, along with impulse response functions and variance decomposition, were utilized to analyze causal and shock transmission relationships between the U.S. and Chinese soybean futures markets during different time periods. The study defined three time periods: pre-COVID-19 (March 26, 2018 - January 17, 2020), pure COVID-19 (January 18, 2020 - February 23, 2021), and war period (February 24, 2021 - November 28, 2023). Optimal VAR lags were determined using LR statistics, FPE, AIC, HQIC, and SBIC.

The Johansen cointegration test revealed no long-term association between the U.S. and Chinese markets for any of the three commodities. Wavelet coherence analysis (WCA) showed that both COVID-19 and the Russia-Ukraine war significantly altered the correlation and causality between U.S. and Chinese soybean and corn futures returns, especially at shorter investment horizons. The war changed the correlation between U.S. and Chinese corn futures from positive to negative but did not affect the lead-lag relationship. The impact on cotton futures was minimal. TVP-VAR analysis demonstrated that U.S. agricultural futures markets consistently transmitted more volatility risk to China than vice-versa. The Russia-Ukraine war significantly increased risk spillover compared to the pandemic, especially for soybean and corn, while the effects on cotton were less pronounced. VAR model analysis of soybean futures showed that the Chinese market's response to U.S. shocks was stronger and more persistent during the war than during the COVID-19 period. Variance decomposition highlighted that the U.S. soybean market substantially influenced Chinese market fluctuations, with this impact amplified during the war.

The findings support contagion theory by demonstrating the transmission of market shocks between U.S. and Chinese agricultural futures markets. The differing impacts on soybean, corn, and cotton markets underscore the commodity-specific nature of these linkages. The greater volatility spillover from U.S. to Chinese markets suggests a dominant influence of U.S. markets on the Chinese agricultural futures market. The amplified impact of the Russia-Ukraine war highlights the vulnerability of global agricultural markets to geopolitical instability and disruptions in key exporting regions. The study's findings add to the literature on commodity market interconnectedness and risk spillover during crises.

This study provides crucial insights into the impact of COVID-19 and the Russia-Ukraine war on the connectedness of U.S. and Chinese agricultural futures markets. Both events altered market dynamics, but the war had a more pronounced effect. The findings have implications for policymakers and investors, who can use this information to inform policy development and investment strategies. Further research could expand the analysis to include other international markets and commodity classes.

The study's focus on three specific commodities might not capture the full complexity of agricultural market interactions. The time period examined may not fully represent all possible scenarios. Further research incorporating additional data and methodologies could enhance the study's scope and accuracy.