Correlation structure analysis of the global agricultural futures market

Dai, Yun-Shi; Huynh, Ngoc Quang Anh; Zheng, Qinghuan; Zhou, Wei‐Xing

doi:10.1016/j.ribaf.2022.101677

Cited by 5 publications

(4 citation statements)

References 53 publications

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“…The decision to use the Mann-Kendall test and Pearson's correlation coefficient (r) was based on the need for an initial analysis of trends and the correlation between variables, being a preliminary stage for future studies that may employ more sophisticated models [63][64][65][66]. It was also based on their applicability and research into other variables, such as air humidity and soil moisture reserves.…”

Section: Discussionmentioning

confidence: 99%

The Relationship between Climate, Agriculture and Land Cover in Matopiba, Brazil (1985–2020)

Araújo,

Rufino,

Silva

et al. 2024

Sustainability

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Climate change has been at the forefront of discussions in the scientific, economic, political, and public spheres. This study aims to analyze the impacts of climate change in the Matopiba region, assessing its relationship with land cover and land use, soybean crop production and yield, and ocean–atmosphere anomalies from 1985 to 2020. The analysis was conducted in four parts: (1) trends in annual and intra-annual climate changes, (2) the spatiotemporal dynamics of land cover and use, (3) the spatiotemporal dynamics of soybean production and yield, and (4) the relationship between climate change, agricultural practices, land cover and use, and ocean–atmosphere anomalies. Statistical analyses, including Mann–Kendall trend tests and Pearson correlation, were applied to understand these relationships comprehensively. The results indicate significant land cover and use changes over 35 years in Matopiba, with municipalities showing increasing soybean production and yield trends. There is a rising trend in annual and intra-annual maximum temperatures, alongside a decreasing trend in annual precipitation in the region. Intra-annual climate trends provide more specific insights for agricultural calendar planning. No correlation was found between the climate change trends and soybean production and yield in the evaluated data attributed to genetic and technological improvements in the region. The North Atlantic Ocean shows a positive correlation with soybean agricultural variables. Evidence suggests soybean production and yield growth under climate change scenarios. This study highlights soybeans’ adaptation and climate resilience in the Matopiba region, providing valuable insights for regional agricultural development and contributing to further research in environmental, water-related, social, and economic areas of global interest.

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Section: Discussionmentioning

confidence: 99%

The Relationship between Climate, Agriculture and Land Cover in Matopiba, Brazil (1985–2020)

Araújo,

Rufino,

Silva

et al. 2024

Sustainability

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“…Among the commodities, agricultural markets are increasingly active, and the linkages among agricultural markets in different countries/regions are increasingly closer [ 5 ]. For example, Hernandez et al [ 26 ] show the volatility spillovers in major agricultural futures markets for corn, wheat, and soybeans.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In recent years, cross-border risk spillovers in the international agricultural futures markets have attracted much attention [5]. Previous literature extensively estimates the co-movements between the US and Chinese agricultural futures markets [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Extreme risk spillovers between US and Chinese agricultural futures markets in crises: A dependence-switching copula-CoVaR model

Hu,

Zhu,

Zhang

et al. 2024

PLoS ONE

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The linkages between the US and China, the world’s two major agricultural powers, have brought great uncertainty to the global food markets. Inspired by these, this paper examines the extreme risk spillovers between US and Chinese agricultural futures markets during significant crises. We use a copula-conditional value at risk (CoVaR) model with Markov-switching regimes to capture the tail dependence in their pair markets. The study covers the period from January 2006 to December 2022 and identifies two distinct dependence regimes (stable and crisis periods). Moreover, we find significant and asymmetric upside/downside extreme risk spillovers between the US and Chinese markets, which are highly volatile in crises. Additionally, the impact of international capital flows (the financial channel) on risk spillovers is particularly pronounced during the global financial crisis. During the period of the COVID-19 pandemic and the Russia-Ukraine 2022 war, the impact of supply chain disruptions (the non-financial channel) is highlighted. Our findings provide a theoretical reference for monitoring the co-movements in agricultural futures markets and practical insights for managing investment portfolios and enhancing food market stability during crises.

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“…The expediency of integration in the agricultural sector with European countries brings certain economic benefits to Ukrainian agricultural producers. The European market is the most expensive, profitable, and characterized by high-quality standardized products [3].…”

mentioning

confidence: 99%

Monitoring of Agricultural Markets in Europe

Parkomenko

2023

Ефективність Міжнародної Економічної Інтеграції

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Correlation structure analysis of the global agricultural futures market

Cited by 5 publications

References 53 publications

The Relationship between Climate, Agriculture and Land Cover in Matopiba, Brazil (1985–2020)

The Relationship between Climate, Agriculture and Land Cover in Matopiba, Brazil (1985–2020)

Extreme risk spillovers between US and Chinese agricultural futures markets in crises: A dependence-switching copula-CoVaR model

Monitoring of Agricultural Markets in Europe

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