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- W3135125989 abstract "Weather risk affects economy, agricultural production in particular. Index insurance has been proposed to hedge against severe weather risk, but large basis risk and low demand accompany current piecewise-linear index insurance contracts. We propose embedding a neural network-based optimization scheme into an expected utility maximization problem to design the index insurance. Neural networks capture the highly nonlinear relationship between the high-dimensional weather variables and production losses. We endogenously solve for the optimal premium and demand of this index insurance. We test this index insurance using corn production loss and weather data in Illinois. We show that this index insurance reduces basis risk, increases farmers' utility, is less reliant on government subsidies, and improves social welfare." @default.
- W3135125989 created "2021-03-15" @default.
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- W3135125989 date "2020-01-01" @default.
- W3135125989 modified "2023-09-30" @default.
- W3135125989 title "Neural Network-based Design of Index Insurance" @default.
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- W3135125989 doi "https://doi.org/10.2139/ssrn.3539811" @default.
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