Uncovering the Sino‐US Dynamic Risk Spillovers Effects: Evidence From Agricultural Futures Markets

• Published date: 01 December 2024
• Author: Han‐Yu Zhu,Peng‐Fei Dai,Wei‐Xing Zhou
• Date: 01 December 2024
• DOI: http://doi.org/10.1002/fut.22551

Journal of Futures Markets

RESEARCH ARTICLE

Uncovering the Sino‐US Dynamic Risk Spillovers Effects:

Evidence From Agricultural Futures Markets

Han‐Yu Zhu

1

| Peng‐Fei Dai

2,3

| Wei‐Xing Zhou

1,4,5

1

School of Business, East China University of Science and Technology, Shanghai, China |

2

School of Management, Wuhan University of Technology, Wuhan,

China |

3

Research Institute of Digital Governance and Management Decision Innovation, Wuhan University of Technology, Wuhan, China |

4

Research

Center for Econophysics, East China University of Science and Technology, Shanghai, China |

5

School of Mathematics, East China University of Science and

Technology, Shanghai, China

Correspondence: Peng‐Fei Dai (pfdai@whut.edu.cn) | Wei‐Xing Zhou (wxzhou@ecust.edu.cn)

Received: 23 March 2024 | Revised: 21 June 2024 | Accepted: 6 August 2024

Funding: This study was supported by the Shanghai Outstanding Academic Leaders Plan, National Natural Science Foundation of China (72201099,

72171083), and the Fundamental Research Funds for the Central Universities.

Keywords: agricultural futures | risk spillover networks | risk spillovers | spillover index | TVP‐VAR‐DY

ABSTRACT

With economic globalization and the financialization of agricultural products continuing to advance, the interconnections

between different agricultural futures have become closer. We utilize a TVP‐VAR‐DY model combined with the quantile

method to measure the risk spillover between 11 agricultural futures in the United States and China from July 9, 2014, to

December 31, 2022. We obtain several findings. First, CBOT corn, soybean, and wheat are identified as the primary risk

transmitters, with DCE corn and soybean as the main risk receivers. Second, sudden events or increased economic uncertainty

can enlarge the overall risk spillovers. Third, there is an aggregation of risk spillovers amongst agricultural futures based on the

dynamic directional spillovers. Lastly, the central agricultural futures under the conditional mean are CBOT corn and soybean,

while CZCE hard wheat and long‐grained rice are the two risk‐spillover centers in extreme cases, as per the results of the

spillover network and minimum spanning tree.

JEL Classification: C32, G15, Q14

1 | Introduction

The production and trade of agricultural products are closely

related to people's daily lives, especially when it comes to

food, but food crises are frequent. From 2000 to 2004, there

were about 850 million people affected by hunger in the

world; in 2010, the number of people affected by hunger in

the world reached one billion; and in April 2022, due

to the COVID‐19 pandemic and the Russia–Ukraine conflict,

the United Nations World Food Program announced that

humanity may face “the largest food crisis since World War

II.”The food crisis is usually characterized by excessive price

increases. Therefore, this paper focuses on the price risk of

agricultural products.

The mitigation of agricultural product price fluctuations through

hedging has contributed to the emergence and evolution of

agricultural futures contracts, marking their pioneering role in

global futures markets. Commodity markets consist mainly of

spot markets and futures markets. The spot market is more

fragmented, and price collection is difficult, whereas futures are

standardized contracts, usually traded on exchanges, whose

prices reflect marketbeliefs and are observed in a timely manner.

Therefore, commodity futures prices havebeen recognized as the

most authoritative and widely used reference prices in the mar-

ket. Despite the growth of futures markets and the expansion of

trading options,agricultural futures remain a criticalcomponent,

making indispensable contributions to the broader futures and

financial landscapes. Presently, an extensive array of over

© 2024 Wiley Periodicals LLC.

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https://doi.org/10.1002/fut.22551

70 agricultural products is actively traded across global futures

markets, categorized into 4 main segments: staple foods, cash

crops, livestock, and forest products. Remarkably, agricultural

futures have witnessed substantial trading activity, surpassing

even the trading volume of agricultural spot markets, as indi-

cated by the American Futures Association's data, which re-

ported 1.815 billion lots of global agricultural futures traded in

2021, accounting for 9% of the total global futures trading vol-

ume. Large tradingvolume indicates that the price of agricultural

futures is informative, so our study focuses on agricultural fu-

tures price risk.

Futures markets serve a fundamental role in price discovery

and hedging, enabling the establishment of reference prices for

the spot market and facilitating price exploration within it.

When it comes to agricultural products, price discovery exhibits

a distinct unidirectional flow from the futures market to the

spot market. This dynamic imparts the potential for the trans-

mission of price‐related risks into the spot market. Therefore,

the study of the price risk of agricultural futures is pioneering,

conducive to the control of the price risk of the spot, and of

practical significance. However, since 2004, the commodity

market has witnessed a notable influx of financial capital,

leading to the financialization of commodities, including agri-

cultural products. This integration of financial instruments has

intensified the connectedness of price fluctuations across vari-

ous agricultural commodity futures markets. Furthermore,

changes in the import and export trade volume and structure of

agricultural spot markets contribute to heightened price risk in

agricultural futures. Taking corn as an example, global imports

and exports of corn reached 1,787,579,000 tons and 200,443,000

tons in 2021, an increase of 27.45% and 65.94% from 2015,

respectively. The intricate dynamics of price volatility and the

transmission of risk events are further compounded by the

continuous financialization of agricultural products, resulting

in increasingly complex mechanisms.

The phenomenon of risk transmission from one market to other

markets is called “risk spillover.”In the context of economic

globalization and financial integration, the degree of correlation

between different types of agricultural futures in different

regions is increasing. As a result, the volatility of the entire

agricultural futures market increases when a certain shock oc-

curs, and they are more likely to interact with each other,

leading to sharp increases and decreases in the same direction

and increasing the risk of the entire market. The linkage

between risk transmission has become more apparent since the

beginning of the 21st century. For example, from 2006 to 2008,

due to the impact of extreme weather, agricultural production

decreased, and coupled with the rising cost of crude oil prices,

agricultural futures prices generally increased. CBOT corn

prices reached a maximum increase of 274% in June 2008, and

CBOT wheat prices reached a maximum increase of 323% in

February 2008, which had a large impact on the entire agri-

cultural futures market. The rising prices of agricultural prod-

ucts, in turn, had a serious impact on developing countries,

with a number of import‐dependent countries experiencing

“food riots”one after another, resulting in casualties.

Various methods are employed to investigate risk spillover in

academic research, including quantile regression, multivariate

GARCH, copula models, and vector autoregression. For the

present study, which focuses on assessing both average price

risk spillover and extreme price risk spillover among agricul-

tural futures, it is essential to utilize a model that adequately

captures the measurement requirements concerning direction,

intensity, and the time‐varying nature of risk spillover due to

the normalization of shocks. In this study, we chose to use the

TVP‐VAR‐DY model, supplemented by the quantile VAR, to

construct the DY spillover index. This index serves to measure

the price risk spillover of agricultural futures by examining the

time‐varying characteristics of price risk. Moreover, the model

enables the evaluation of the intensity and direction of risk

spillovers from one market to all other markets, as well as

pairwise risk spillovers between any two markets, thus pro-

viding a comprehensive measurement of price risk at the

aggregate systemic level.

With the continuous development of economic globalization,

the interdependence among nations has intensified, leading to

accelerated and amplified transmission of agricultural risk

shocks across countries. This study investigates the dynamics of

risk spillovers from the occurrence of agricultural shocks in one

country to others, with a particular focus on staple food futures

between the United States and China. In the spot market, the

United States holds the position of the world's largest producer

of soybeans and corn and the largest exporter of food, while

China is the largest producer of wheat and rice. Given its

population, China is by far the largest importer of food,

resulting in close import‐export transactions for these agricul-

tural commodities. Furthermore, China's agricultural futures

market has demonstrated remarkable growth, with its top 10

agricultural futures contributing significantly to the global

agricultural futures turnover in 2022, while the growth rate of

the more established US agricultural futures market has shown

signs of deceleration.

Given the evolving landscape of futures markets, this paper

aims to empirically re‐evaluate the nature of risk spillovers

between US and Chinese agricultural futures. By utilizing data

from 11 staple food futures listed in China and the United

States, spanning the period from July 9, 2014, to December 31,

2022, we provide insights into the dynamics of risk transmission

and the implications for market participants and policymakers.

First, our study uses the spillover index model in time domains

to explore the direction and scale of risk transmission between

agricultural futures in China and the United States

markets. Second, our study conducts network analysis based on

the risk spillover index. Third, our study employs the minimum

spanning tree (MST) to identify the shortest risk transmission

path as well as the most influential markets in the process of

risk transmission.

This paper aims to contribute to the literature from the fol-

lowing aspects. First, this paper uses a dynamic measure of

TVP‐VAR‐DY in combination with quantiles, which better

measures risk spillovers in the time dimension and takes into

account risk transmission in extreme cases, helping to protect

against downside risks and also providing a basis for future risk

management in response to extreme events. Second, this paper

applies networks and MST for risk transfer visualization, which

enables market regulators to accurately locate the sources of

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