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Hedging Grain Price Risk through Insurance-plus-Futures: The Case of China's Corn Industry

Author

Listed:
  • Mike S. Li
  • Chengyi Pu

Abstract

This paper evaluates the effectiveness of Insurance-plus-Futures (IPF) programs in hedging grain price risk in China’s corn industry. Using data from four representative IPF models, the study assesses farmer income protection and hedging performance in futures markets. Results show that IPF models significantly mitigate income volatility, with the IPF-plus-Bank model offering the highest compensation rate. However, hedging effectiveness varies across models due to differences in market correlation and volatility. Notably, the integrated IPF-plus-Bank-and-Order model achieves the most robust risk-hedging efficiency by effectively anchoring basis risk. The findings highlight the importance of product design, pricing accuracy, and market infrastructure in enhancing agricultural risk management. The study offers policy insights for improving the scalability and efficiency of agricultural financial innovation.

Suggested Citation

  • Mike S. Li & Chengyi Pu, 2026. "Hedging Grain Price Risk through Insurance-plus-Futures: The Case of China's Corn Industry," International Business Research, Canadian Center of Science and Education, vol. 19(2), pages 1-71, April.
    • Handle: RePEc:ibn:ibrjnl:v:19:y:2026:i:2:p:71
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    References listed on IDEAS

    as
    1. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    2. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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