Managing Weather Risk with a Neural Network-Based Index Insurance

Abstract

Weather risk affects 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 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 premium, and increases farmers' utility. Our approach can be generalized to design other financial products.

Keywords: weather risk; index insurance; basis risk; neural networks; machine learning

JEL Classification: C45, G11, G22, G52, O13, Q54