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Global agricultural vulnerability to climate physical risks

Author

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  • Yang, Hao
  • Yang, Jie
  • Feng, Yun

Abstract

Climate change intensifies extreme weather events and natural disasters, posing escalating threats to global food security. This study quantifies the vulnerability of five key agricultural commodities—wheat, maize, soybean, rice, and barley—to climate physical risks during 2000–2025, specifically examining their interconnected market responses. Utilizing the TVP-VAR extended joint connectedness approach, we find wheat acts as the leading transmitter of risk spillovers, while barley is the primary recipient. Rice exhibits relative isolation within the risk connectedness network. Applying cross-quantilogram analysis further reveals that climate physical risks exacerbate uncertainty across the agricultural sector, a pattern observed consistently whether at multiple time scales or different cross-quantiles. Crucially, excluding rice, physical risks significantly enhance the risk transmission capacity of the other four crops, highlighting their heightened vulnerability to climate shocks. These findings underscore the critical need for policymakers and market participants to prioritize resilience-building strategies for these highly interconnected and vulnerable commodities within global food systems.

Suggested Citation

  • Yang, Hao & Yang, Jie & Feng, Yun, 2026. "Global agricultural vulnerability to climate physical risks," Finance Research Letters, Elsevier, vol. 87(C).
  • Handle: RePEc:eee:finlet:v:87:y:2026:i:c:s1544612325022433
    DOI: 10.1016/j.frl.2025.108990
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    1. Cornelis Gardebroek & Manuel A. Hernandez & Miguel Robles, 2016. "Market interdependence and volatility transmission among major crops," Agricultural Economics, International Association of Agricultural Economists, vol. 47(2), pages 141-155, March.
    2. Robert F Engle & Stefano Giglio & Bryan Kelly & Heebum Lee & Johannes Stroebel, 2020. "Hedging Climate Change News," The Review of Financial Studies, Society for Financial Studies, vol. 33(3), pages 1184-1216.
    3. Cui, Xiaomeng & Zhong, Zheng, 2024. "Climate change, cropland adjustments, and food security: Evidence from China," Journal of Development Economics, Elsevier, vol. 167(C).
    4. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Journal of Econometrics, Elsevier, vol. 164(1), pages 188-205, September.
    5. Christopher L. Gilbert, 2010. "How to Understand High Food Prices," Journal of Agricultural Economics, Wiley Blackwell, vol. 61(2), pages 398-425, June.
    6. Sohag, Kazi & Hassan, M. Kabir & Kalina, Irina & Mariev, Oleg, 2023. "The relative response of Russian National Wealth Fund to oil demand, supply and risk shocks," Energy Economics, Elsevier, vol. 123(C).
    7. Li, Yuheng & Gao, Guangya & Wen, Jiuyao & Zhao, Ning & Du, Guoming & Stanny, Monika, 2025. "The measurement of agricultural disaster vulnerability in China and implications for land-supported agricultural resilience building," Land Use Policy, Elsevier, vol. 148(C).
    8. Guo, Kun & Li, Yichong & Zhang, Yunhan & Ji, Qiang & Zhao, Wanli, 2023. "How are climate risk shocks connected to agricultural markets?," Journal of Commodity Markets, Elsevier, vol. 32(C).
    9. Carleton, Tamma A & Hsiang, Solomon M, 2016. "Social and economic impacts of climate," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt2vz2d2zz, Department of Agricultural & Resource Economics, UC Berkeley.
    10. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    11. Nam, Kyungsik, 2021. "Investigating the effect of climate uncertainty on global commodity markets," Energy Economics, Elsevier, vol. 96(C).
    12. Zouhaier Dhifaoui & Rabeh Khalfaoui & Sami Ben Jabeur & Mohammad Zoynul Abedin, 2023. "Exploring the effect of climate risk on agricultural and food stock prices: Fresh evidence from EMD-Based variable-lag transfer entropy analysis," Post-Print hal-03998224, HAL.
    13. Yuqin Zhou & Shan Wu & Zhenhua Liu & Lavinia Rognone, 2023. "The asymmetric effects of climate risk on higher-moment connectedness among carbon, energy and metals markets," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    14. Fernandez-Perez, Adrian & Fuertes, Ana-Maria & Gonzalez-Fernandez, Marcos & Miffre, Joelle, 2020. "Fear of hazards in commodity futures markets," Journal of Banking & Finance, Elsevier, vol. 119(C).
    15. Corey Lesk & Pedram Rowhani & Navin Ramankutty, 2016. "Influence of extreme weather disasters on global crop production," Nature, Nature, vol. 529(7584), pages 84-87, January.
    16. Balcilar, Mehmet & Gabauer, David & Umar, Zaghum, 2021. "Crude Oil futures contracts and commodity markets: New evidence from a TVP-VAR extended joint connectedness approach," Resources Policy, Elsevier, vol. 73(C).
    17. repec:hal:journl:peer-00844811 is not listed on IDEAS
    18. Wang, Di & Zhang, Peng & Chen, Shuai & Zhang, Ning, 2024. "Adaptation to temperature extremes in Chinese agriculture, 1981 to 2010," Journal of Development Economics, Elsevier, vol. 166(C).
    19. Marco Del Negro & Giorgio E. Primiceri, 2015. "Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1342-1345.
    20. Han, Heejoon & Linton, Oliver & Oka, Tatsushi & Whang, Yoon-Jae, 2016. "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series," Journal of Econometrics, Elsevier, vol. 193(1), pages 251-270.
    21. Lu, Peng & Wang, Ziwei & Lu, Kun, 2025. "Climate Disaster, Investor Attention, and Tail Risk: Graph-based CoVaR," Economics Letters, Elsevier, vol. 253(C).
    22. Tomoko Hasegawa & Shinichiro Fujimori & Petr Havlík & Hugo Valin & Benjamin Leon Bodirsky & Jonathan C. Doelman & Thomas Fellmann & Page Kyle & Jason F. L. Koopman & Hermann Lotze-Campen & Daniel Maso, 2018. "Risk of increased food insecurity under stringent global climate change mitigation policy," Nature Climate Change, Nature, vol. 8(8), pages 699-703, August.
    23. Yang, Jie & Yang, Hao & Feng, Yun, 2025. "Quantifying the geopolitical risk resilience of commodity futures markets," Economics Letters, Elsevier, vol. 247(C).
    24. Diebold, Francis X. & Yılmaz, Kamil, 2014. "On the network topology of variance decompositions: Measuring the connectedness of financial firms," Journal of Econometrics, Elsevier, vol. 182(1), pages 119-134.
    25. Lastrapes, William D. & Wiesen, Thomas F.P., 2021. "The joint spillover index," Economic Modelling, Elsevier, vol. 94(C), pages 681-691.
    26. Timmer, C. Peter, 2010. "Reflections on food crises past," Food Policy, Elsevier, vol. 35(1), pages 1-11, February.
    27. Faccini, Renato & Matin, Rastin & Skiadopoulos, George, 2023. "Dissecting climate risks: Are they reflected in stock prices?," Journal of Banking & Finance, Elsevier, vol. 155(C).
    28. Thompson, Wyatt & Lu, Yaqiong & Gerlt, Scott & Yang, Xianyu & Campbell, J. Elliott & Kueppers, Lara M. & Snyder, Mark A., 2018. "Automatic Responses of Crop Stocks and Policies Buffer Climate Change Effects on Crop Markets and Price Volatility," Ecological Economics, Elsevier, vol. 152(C), pages 98-105.
    29. Marc F. Bellemare & Christopher B. Barrett & David R. Just, 2013. "The Welfare Impacts of Commodity Price Volatility: Evidence from Rural Ethiopia," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(4), pages 877-899.
    30. Sohag, Kazi & Hassan, M. Kabir & Bakhteyev, Stepan & Mariev, Oleg, 2023. "Do green and dirty investments hedge each other?," Energy Economics, Elsevier, vol. 120(C).
    31. Feng, Yun & Yang, Jie & Huang, Qian, 2023. "Multiscale correlation analysis of Sino-US corn futures markets and the impact of international crude oil price: A new perspective from the multifractal method," Finance Research Letters, Elsevier, vol. 53(C).
    32. Yang, Jie & Feng, Yun & Yang, Hao, 2024. "Scrutinizing multi-scale and multi-quantile interactions in commodity markets: A petrochemical industrial chain perspective," Energy Economics, Elsevier, vol. 140(C).
    33. Just, Małgorzata & Echaust, Krzysztof, 2022. "Dynamic spillover transmission in agricultural commodity markets: What has changed after the COVID-19 threat?," Economics Letters, Elsevier, vol. 217(C).
    34. Flori, Andrea & Pammolli, Fabio & Spelta, Alessandro, 2021. "Commodity prices co-movements and financial stability: A multidimensional visibility nexus with climate conditions," Journal of Financial Stability, Elsevier, vol. 54(C).
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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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