Journal article
North American Actuarial Journal · 2024-01-02 · Volume 28 · Issue 1 · pp. 57-72
DOI: 10.1080/10920277.2023.2182792
Published online: 2023-04-11 · Download citation (BibTeX)
Abstract
Effective agricultural insurance and risk management program rely on accurate crop yield forecasting. In this paper, we propose a novel deep factor model for crop yield forecasting and crop insurance ratemaking. This framework first utilizes a deep autoencoder to extract a latent factor, called production index, which integrates salient spatial temporal patterns in the original yield data. Then, a concatenated deep learning model is constructed to enhance the modeling of the production index and the reconstruction of crop yields. Convolutional neural networks are employed to capture the high-dimensional and highly nonlinear structure within the crop yield data, as well as its interactions with weather and economic variables. The proposed deep factor framework is applied to the county-level data in the state of Iowa, U.S. Empirical results show that the newly proposed deep factor model significantly improves the prediction accuracy, especially in the test set. Based on a retain-cede crop insurance rating game between a private insurer and the government, we show that the proposed deep factor model provides economically and statistically significant improvement over the current RMA ratemaking methodology.
Keywords: agricultural insurance; autoendocer; convolutional neural network; deep learning; ratemaking