Managing Weather Risk with a Neural Network-Based Index Insurance

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. This paper was accepted by Agostino Capponi, finance. Funding: This work was supported by the Research Grants Council, University Grants Committee [Grants GRF 16502020, GRF 16504522, and T31-603/21-N], Singapore Ministry of Education Academic Research Fund Tier 1 [Grants RG143/19 and RG55/20], the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2021-04144 and DGECR-2021-00330], the Research Database Matching Fund, and the School of Business and Management, Hong Kong University of Science and Technology. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4902 .