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Dynamic Connectedness between Energy and Agricultural Commodity Markets in China: Evidence from a Generalized VAR Framework

Author(s)

Chaofeng Tang*, Tao Liu

Affiliation(s)

Graduate School of Humanities and Management, Guangdong Medical University, Dongguan, Guangdong, China *Corresponding Author

Abstract

This study examines dynamic return-shock connectedness between China's energy and agricultural commodity markets using daily spot prices for WTI crude oil, thermal coal, liquefied natural gas (LNG), corn, cotton, soybean meal, and white sugar from 24 October 2013 to 12 January 2026. A VAR model with generalized forecast error variance decompositions (GFEVDs) is used to construct the Diebold–Yilmaz connectedness measures. The full-sample matrix shows asymmetric spillovers: thermal coal has the largest net spillover (1.03), followed by soybean meal (0.80), white sugar (0.59), WTI crude oil (0.57), and corn (0.26), while cotton (-2.61) and LNG (-0.65) are net receivers. The sum of off-diagonal shares is 38.38, which corresponds to a conventional normalized total connectedness index of 5.48%. Rolling connectedness rises materially around the 2016 corn reserve reform, the 2018 China–US trade frictions, COVID-19, the 2021 dual-control policy, and the Russia–Ukraine conflict. In particular, WTI-to-soybean meal spillovers are persistently positive after 2019, while coal-to-soybean meal spillovers are highly event-sensitive. The results identify coal and crude oil as important risk-transmission channels and soybean meal as a key intermediary within the domestic commodity system.

Keywords

Commodity Markets; Generalized Forecast Error Variance Decomposition; Connectedness; Energy–Agriculture Linkage; Diebold–Yilmaz

References

[1] Dong, X., Shuai, W., & Zhao, Z. (2014). An empirical study of the impact of crude oil price changes on China’s food prices. China Soft Science, (10), 129–143. (in Chinese) [2] Wang, G., & Zhao, X. (2022). The impact of the drastic fluctuation of international crude oil market price on China's grain market: Based on an analysis of counterfactual simulation scenario. Journal of Hunan Agricultural University (Social Sciences), 23(6), 28–38. (in Chinese) [3] Yang, M. (2024). Time-frequency evolution of multi-price volatility between energy and food markets. Doctoral dissertation, China University of Geosciences (Beijing). (in Chinese) [4] Guo, C. (2025). Research on the Price Connection and Effects Among Futures, Options and Spots in the Chinese Agricultural Commodity Market. Doctoral dissertation, Huazhong Agricultural University. (in Chinese) [5] Liu, Y., Fang, Y., & Yang, J. (2024). Cross-market risk contagion and external shocks of China’s food energy. Chinese Management Science, 32(11), 103–114. (in Chinese) [6] Du, X., Yu, C. L., & Hayes, D. J. (2011). Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis. Energy Economics. https://doi.org/10.1016/j.eneco.2010.12.015 [7] Gardebroek, C., & Hernandez, M. A. (2013). Do energy prices stimulate food price volatility? Examining volatility transmission between US oil, ethanol and corn markets. Energy Economics. https://doi.org/10.1016/j.eneco.2013.06.013 [8] Nazlioglu, S., Erdem, C., & Soytas, U. (2013). Volatility spillover between oil and agricultural commodity markets. Energy Economics. https://doi.org/10.1016/j.eneco.2012.11.009 [9] Ji, Q., Bouri, E., Roubaud, D., & others. (2018). Risk spillover between energy and agricultural commodity markets: A dependence-switching CoVaR-copula model. Energy Economics. https://doi.org/10.1016/j.eneco.2018.08.015 [10] Dahl, R. E., Oglend, A., & Yahya, M. (2020). Dynamics of volatility spillover in commodity markets: Linking crude oil to agriculture. Journal of Commodity Markets. https://doi.org/10.1016/j.jcomm.2019.100111 [11] Gong, X., & Xu, J. (2022). Geopolitical risk and dynamic connectedness between commodity markets. Energy Economics. https://doi.org/10.1016/j.eneco.2022.106028 [12] Vo, D. H., & Tran, M. P. B. (2024). Volatility spillovers between energy and agriculture markets during the ongoing food & energy crisis: Does uncertainty from the Russo-Ukrainian conflict matter? Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2024.123723 [13] Zhang, T., Li, P. F., & Zhou, W. X. (2025). Spillover effects between climate policy uncertainty, energy markets, and food markets: A time–frequency analysis. Finance Research Letters. https://doi.org/10.1016/j.frl.2025.107553 [14] Diebold, F. X., & Yilmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal. https://doi.org/10.1111/j.1468-0297.2008.02208.x [15] Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2011.02.006 [16] Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics. https://doi.org/10.1016/j.jeconom.2014.04.012