A deep factor model for crop yield forecasting and insurance ratemaking

Effective agricultural insurance and risk management programs rely on accurate crop yield forecasting. In this article, a novel deep factor model for crop yield forecasting and crop insurance ratemaking is proposed. This framework first utilizes a deep autoencoder to extract a latent factor, called...

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Main Author: Zhu, Wenjun
Other Authors: Nanyang Business School
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/170183
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1701832023-08-31T01:18:57Z A deep factor model for crop yield forecasting and insurance ratemaking Zhu, Wenjun Nanyang Business School Business::Finance Crop Insurance Weather Derivatives Effective agricultural insurance and risk management programs rely on accurate crop yield forecasting. In this article, a novel deep factor model for crop yield forecasting and crop insurance ratemaking is proposed. This framework first utilizes a deep autoencoder to extract a latent factor, called the production index, that 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. 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 Risk Management Agency ratemaking methodology. Ministry of Education (MOE) Nanyang Technological University The author is grateful for the research funding support from the Nanyang Technological University Start-Up Grant (04INS000384C300), Singapore Ministry of Education Academic Research Fund Tier 1 (RG143/19), and the Society of Actuaries Education Institution Grant. 2023-08-31T01:18:57Z 2023-08-31T01:18:57Z 2023 Journal Article Zhu, W. (2023). A deep factor model for crop yield forecasting and insurance ratemaking. North American Actuarial Journal. https://dx.doi.org/10.1080/10920277.2023.2182792 1092-0277 https://hdl.handle.net/10356/170183 10.1080/10920277.2023.2182792 2-s2.0-85152364862 en 04INS000384C300 RG143/19 North American Actuarial Journal © 2023 Society of Actuaries. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Business::Finance
Crop Insurance
Weather Derivatives
spellingShingle Business::Finance
Crop Insurance
Weather Derivatives
Zhu, Wenjun
A deep factor model for crop yield forecasting and insurance ratemaking
description Effective agricultural insurance and risk management programs rely on accurate crop yield forecasting. In this article, a novel deep factor model for crop yield forecasting and crop insurance ratemaking is proposed. This framework first utilizes a deep autoencoder to extract a latent factor, called the production index, that 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. 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 Risk Management Agency ratemaking methodology.
author2 Nanyang Business School
author_facet Nanyang Business School
Zhu, Wenjun
format Article
author Zhu, Wenjun
author_sort Zhu, Wenjun
title A deep factor model for crop yield forecasting and insurance ratemaking
title_short A deep factor model for crop yield forecasting and insurance ratemaking
title_full A deep factor model for crop yield forecasting and insurance ratemaking
title_fullStr A deep factor model for crop yield forecasting and insurance ratemaking
title_full_unstemmed A deep factor model for crop yield forecasting and insurance ratemaking
title_sort deep factor model for crop yield forecasting and insurance ratemaking
publishDate 2023
url https://hdl.handle.net/10356/170183
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