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

Abstract

Area-yield insurance has gained much attention in acdademy and practice due to its capability of addressing 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. The objective of this paper is to reduce the basis risk of area-yield crop insurance by finding the optimal risk pooling using machine learning algorithms. Two clustering algorithms, including the K-means algorithm and the Gaussian Mixture Model (GMM) optimized with Expectation-Maximization (EM) algorithm, are investigated and compared in searching alternative grouping methods. Five model selection algorithms are utilized to determine the optimized number of risk pools by taking the weather, location, and historical yield information into consideration. The five model selection algorithms are Elbow Point of Within Group Sum of Squares (WSS), validity measurement, Average Silhouette Index, Gap Statistic, and Weighted Stochastic Block Model (WSBM). Finally, the proposed optimal risk pooling algorithms are empirically tested and cross-compared with the U.S. corn production and weather data from 12 States in the Midwest region.