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Managing Weather Risk with a Neural Network-Based Index Insurance

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zhanhui Chen & Yang Lu & Jinggong Zhang & Wenjun Zhu, 2024. "Managing Weather Risk with a Neural Network-Based Index Insurance," Management Science, INFORMS, vol. 70(7), pages 4306-4327, July.
    • Handle: RePEc:inm:ormnsc:v:70:y:2024:i:7:p:4306-4327
    DOI: 10.1287/mnsc.2023.4902
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