Do Weather Derivatives Mitigate the Revenue Risk of Farmers?—The Case of Tongliao, Inner Mongolia, China

Haoyu Wang and Yuanfeng Zhao ()
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Haoyu Wang: School of Economics and Management, Inner Mongolia Agricultural University, Hohhot 010011, China
Yuanfeng Zhao: School of Economics and Management, Inner Mongolia Agricultural University, Hohhot 010011, China

Sustainability, 2024, vol. 16, issue 3, 1-16

Abstract: This research probes the potential of weather derivatives as tools for mitigating the variability of crop yields due to climatic uncertainties in China. Centered on Tongliao City in Inner Mongolia, the study exploits a long short-term memory (LSTM) network to dissect and simulate 32 years of local precipitation data, thereby achieving a simulation of high reliability. Further exploration through a multiple linear regression model confirms a marked positive relationship between rainfall amounts and maize yields. By combining precipitation put options and the total revenue function for farmers, mathematical derivations yield specific expressions for optimal trading quantities and risk hedging efficiency. The research findings show that, using an assumption of a maize price that is 3 CNY/kg, when farmers purchase around 6.22 precipitation put options they can achieve 67.9% risk hedging efficiency. This highlights the significant role of precipitation put options under specific conditions in reducing the risk of decreased maize yields due to reduced precipitation. However, in practical markets, variations in maize prices and the price change unit (λ) are inevitable. Through further analysis, this study reveals that as these factors change, the optimal trading quantity and hedging efficiency also undergo varying degrees of adjustment. The investigation lays a theoretical groundwork for the practical application and empirical validation of weather derivatives within China’s agrarian sector. However, the study underscores the necessity of a holistic approach to market dynamics to refine hedging strategies. Future decision making must integrate market fluctuations, and adopting transparent pricing mechanisms is critical for enhanced risk management and the advancement of sustainable agricultural practices.

Keywords: weather derivatives; LSTM model; OLS regression; corn yield; weather risk (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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