Managing Weather Risk with a Neural Network-Based Index Insurance

Zhanhui Chen (), Yang Lu (), Jinggong Zhang () and Wenjun Zhu ()
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Zhanhui Chen: Department of Finance, School of Business and Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, China
Yang Lu: Department of Mathematics & Statistics, Concordia University, Montreal, Quebec H3G 1M8, Canada
Jinggong Zhang: Division of Banking and Finance, Nanyang Business School, Nanyang Technological University, Singapore 639798, Singapore
Wenjun Zhu: Division of Banking and Finance, Nanyang Business School, Nanyang Technological University, Singapore 639798, Singapore

Management Science, 2024, vol. 70, issue 7, 4306-4327

Abstract: Weather risk affects the economy, agricultural production in particular. Index insurance is a promising tool to hedge against weather risk, but current piecewise-linear index insurance contracts face large basis risk and low demand. We propose embedding a neural network-based optimization scheme into an expected utility maximization problem to design the index insurance contract. Neural networks capture a highly nonlinear relationship between the high-dimensional weather variables and production losses. We endogenously solve for the optimal insurance premium and demand. This approach reduces basis risk, lowers insurance premiums, and improves farmers’ utility.

Keywords: neural networks; weather risk; index insurance; basis risk; utility maximization (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:70:y:2024:i:7:p:4306-4327

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