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

The increasing globalization of international trade and financial markets has increased the interconnectedness of various commodity markets in the global context (Yang and Zhou, 2020). In the face of strong external shocks, the interconnectedness between international commodity markets can increase the spread of risks (Melvin and Sultan, 1990; Silvennoinen and Thorp, 2013; Webb et al., 2016; Junttila et al., 2018; Song et al., 2018). ; Mensi et al., 2019; Alquist et al., 2020), potentially leading to extreme events such as negative oil futures prices and supply chain disruptions due to coronavirus disease 2019 (COVID-19). Therefore, regulators need to be aware of the international connections of commodity futures markets in order to implement market surveillance measures that stabilize these markets and the global supply chain. In addition, investors can benefit from understanding and predicting the relationships and risk transfer between international commodity futures markets as this helps them develop effective international portfolio investment and hedging strategies (Fernandez-Perez et al., 2020, 2022).

When the global financial crisis broke out in 2008, numerous studies examined how external events or crises affect relationships between developed and emerging markets (Yue et al., 2020; Li and Majerowska, 2008; Wu et al., 2017 ; Wang et al., 2020). However, there is little research on the interconnectedness of international commodity markets during global crises such as the COVID-19 pandemic and the Russia-Ukraine conflict. Although previous studies have examined price movements between the wheat futures markets in the United States and Canada (Booth et al., 1998) as well as price cointegration between the copper futures markets in London and Shanghai (Li and Zhang, 2008), there were There has been such a lack of exploration of international commodity market connections during recent global crises. Recently, scholars have focused on the impact of the COVID-19 pandemic, which has increased the interdependence between the US and Chinese stock and oil futures markets (Zhang and Mao, 2022; Zhang et al., 2022). In addition, COVID-19 and the Russia-Ukraine war have different impacts on the connection between the US and Chinese stock markets (Zhang and Sun, 2023). This article aims to examine how COVID-19 and the Russia-Ukraine conflict impact the causal and correlational relationships between the US and Chinese agricultural futures markets.

The motivation of this paper is fourfold. First, price movements in agricultural futures can affect other futures prices in different commodity families, including energy and metals families (Batten et al., 2015; Bonato, 2019; Byrne et al., 2013; Pindyck and Rotemberg, 1990; Zhang and Ding, 2018, 2021; Zhang et al., 2018, 2019; Ding and Zhang, 2020). Second, agricultural commodities are important to both the US and China. The United States is the largest agricultural producer and exporter, while China is the largest agricultural consumer and importer. Therefore, understanding the connection between the US and Chinese agricultural futures markets is important not only for cross-market risk hedging, but also for the agricultural economy in both countries. Third, the global economy and commodity price fluctuations have been significantly affected by the COVID-19 pandemic, which is a public health emergency (Zhang and Wang, 2022). The pandemic severely affected agricultural commodity production due to global lockdowns. Many agricultural exporting countries in Southeast Asia and India have restricted exports of agricultural commodities due to severe supply shortages in meeting domestic food needs. The pandemic has also made Chinese agricultural consumption more dependent on imports from the US market than before. After all, Ukraine is an important exporter of agricultural raw materials. The war in Ukraine has dramatically affected global agricultural trade and significantly increased food prices in most parts of the world.

Contagion theory can provide a valuable framework to support the connectivity of the US-China agricultural futures market by demonstrating that the propagation of market shocks in one country's agricultural futures market can be transmitted to the other country, affecting both futures prices as well as the dynamics of the futures market (Pericoli and Sbracia, 2003). A contagion event can occur through various channels, such as information flows and financial connections.

The flow of information plays a crucial role in the contagion of financial markets. Globalization and technological advances have facilitated the rapid dissemination of information and made futures markets more interconnected than ever before. News of a shock in one country can quickly spread to other countries, influencing market sentiment and triggering financial contagion effects in futures markets (Yarovaya et al., 2022). In the context of the soybean and corn futures markets, a country's release of major agricultural reports or trade policies can have a significant impact on the dynamics of the agricultural futures market. If negative news about soy and corn production or trade restrictions breaks in one country, it can cause panic in the other country's market, leading to price volatility and contagion effects.

In addition, the financial ties between the US and China are also contributing to possible contagion in the agricultural futures market. Financial institutions, investors and speculators from both countries participate in the commodity futures markets, thereby creating connections between the futures markets of both countries. These linkages can enhance the transmission of shocks and risks through various investment instruments, including commodity futures contracts, and enable risk-taking in the agricultural futures market (Caramazza et al., 2004). The transfer of cross-border risk in futures markets typically occurs through capital and investor sentiment channels based on financial connections. During a period of global financial integration, a country experiencing a liquidity collapse in its futures market may lead to similar challenges for its trading partners, thereby spreading risks across futures markets (Patel et al., 2022). In addition, the sentiment of international investors also serves as an important risk diversification channel for risk contagion in the futures market. For example, when a country's agricultural futures market becomes risky, global investors tend to take a bearish attitude towards investing in that country's agricultural futures market. This negative sentiment could also extend to other countries' agricultural futures markets, leading to lower investments in agricultural futures markets such as the soybean futures market (Akyildirim et al., 2022).

Our study was conducted on three major agricultural futures: soybeans, corn and cotton, which are the most actively traded in both the US and Chinese futures markets. We first apply the Johansen cointegration test to examine the existence of a long-run relationship between US and Chinese agricultural futures returns. In addition, we use wavelet coherence analysis (WCA) to examine the co-movement and lead-lag relationships (causal relationships) between US and Chinese agricultural futures markets at different time scales and time locations. The WCA method is a powerful tool for detecting localized co-movements between different time series with multiscale structures and has been successfully applied in recent economic research (Marobhe and Kansheba, 2023, Umar and Gubareva, 2021). We then apply the connectedness approach of time-varying parameter vector autoregression (TVP-VAR) (Cagli et al., 2023) to examine the impact of the COVID-19 pandemic and the Russia-Ukraine war on the dynamic volatility transfers between the US to investigate and Chinese markets. Finally, we analyze the impact of exogenous shocks during the COVID-19-only period and the Russia-Ukraine war period on risk transfer in the US and Chinese soybean futures markets using the impulse response function and the variance decomposition method within the framework of vector autoregression (VAR ) model frame.

Our article contributes to the commodity market research literature in the following ways. First, the COVID-19 outbreak at the beginning of 2020 and the Russia-Ukraine war around spring 2022 have changed both the correlation relationship and the causal relationship between the US and Chinese soybean futures markets in short-term investment periods. Second, the Russia-Ukraine war has turned from positive to negative correlation without changing the causal relationship (lead-lag relationship) between US and Chinese corn futures returns on a short time scale of up to 12 days, while COVID-19 has had little impact on the connection between two corn futures markets. Third, both COVID-19 and the Russia-Ukraine War had little impact on the causal relationships determined by U.S. cotton futures returns on short time scales and exhibited positive correlation relationships on longer time scales. Fourth, the pandemic lockdown had a temporary impact on preventing risk transfer to the U.S. and Chinese soybean and corn futures markets, while the Russia-Ukraine war prevented risk transfer to the U.S. and Chinese soybean and corn futures markets Corn futures markets have increased dramatically in a short period of time. However, these two exogenous events have little impact on risk transfer to the US and Chinese cotton markets. Finally, the Chinese soybean market's impulse response to the US shock is much stronger than the US market's response to the Chinese shock. Cross-market reactions are much more sustained during the Russia-Ukraine war than during the COVID-19 period. The U.S. soybean market's variance contribution to Chinese market fluctuations is much stronger than that of the U.S. market during both the COVID-19 period and the war, while the war has amplified such cross-market variance contributions from China significantly more than those from the USA contributions.

The rest of this paper is organized as follows: The “Data and descriptive statistics” section presents the data and descriptive statistics. The section “Return Comovements between the US and Chinese Agricultural Futures Markets” presents a wavelet coherence analysis (WCA) to examine the co-movement and lead-lag causal relationships between the US and Chinese agricultural futures markets. To examine markets at different time scales and time intervals under the influence of COVID-19 and the Russia-Ukraine War. In the “Dynamic Volatility Linkage Between the U.S. and Chinese Agricultural Futures Markets” section, the TVP-VAR methodology is applied to examine the impact of these external events on the dynamic volatility spillovers between the U.S. and Chinese Agricultural Futures markets. to investigate markets. In the section “The Causal and Risk Transmission Relationships Between the U.S. and Chinese Soybean Futures Markets in Times of Crisis,” VAR models are used to examine the causal and shock transmission relationships between the two soybean futures markets in times of crisis. The “Conclusions, Discussions and Implications” section concludes our paper with discussions and research implications.