Media Optimal Risk Pooling for Area-Yield Insurance: A Behavior-based Machine Learning Approach

Optimal Risk Pooling for Area-Yield Insurance: A Behavior-based Machine Learning Approach

uploaded August 2, 2021 Views: 55 Comments: 0 Favorite: 0 CPD
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Area-yield insurance has gained much attention in academy and practice due to its capability to address information asymmetry and data limitation, as well as its low administrative cost. However, by its index insurance nature, area-yield insurance suffers the problem of basis risk. This paper proposes a producer-bahavior-based machine learning approach for optimal risk pooling to reduce the basis risk. Assuming producers are risk-averse and rational, we determine the optimal number of risk pools by investigating how different risk pool formation will incentivize the producers’ production behaviors. A spectral clustering approach based on the K-means algorithm is utilized to form the optimal risk pools from the high-dimensional dataset. Five risk measurements for producers’ basis risks and insurers’ contingent liabilities are scrutinized and compared. Finally, the proposed optimal risk pooling algorithms are empirically tested and cross-compared with the data from major corn production counties in the US Heartland region.

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