Managing Weather Risk with a Neural Network-Based Index Insurance

• 成果类型: Article
• 署名作者: Chen, Zhanhui; Lu, Yang; Zhang, Jinggong; Zhu, Wenjun
• 署名单位: Hong Kong University of Science & Technology; Concordia University - Canada; Nanyang Technological University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.4902
• 发表日期: 2024
• 页码: 4306-4327
• 关键词: Neural networks; weather risk; Index insurance; basis risk; Utility maximization

摘要

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.