基于可信度的农作物再保险定价与天气风险管理产量预测模型

Wenjun Zhu, Lysa Porth, K. S. Tan
{"title":"基于可信度的农作物再保险定价与天气风险管理产量预测模型","authors":"Wenjun Zhu, Lysa Porth, K. S. Tan","doi":"10.2139/ssrn.2663932","DOIUrl":null,"url":null,"abstract":"\nPurpose\nThe purpose of this paper is to propose an improved reinsurance pricing framework, which includes a crop yield forecasting model that integrates weather variables and crop production information from different geographically correlated regions using a new credibility estimator, and closed form reinsurance pricing formulas. A yield restatement approach to account for changing crop mix through time is also demonstrated.\n\n\nDesign/methodology/approach\nThe new crop yield forecasting model is empirically analyzed based on detailed farm-level data from Manitoba, Canada, covering 216 crop varieties from 19,238 farms from 1996 to 2011. As well, corresponding weather data from 30 stations, including daily temperature and precipitation, are considered. Algorithms that combine screening regression, cross-validation and principal component analysis are evaluated for the purpose of achieving efficient dimension reduction and model selection.\n\n\nFindings\nThe results show that the new yield forecasting model provides significant improvements over the classical regression model, both in terms of in-sample and out-of-sample forecasting abilities.\n\n\nResearch limitations/implications\nThe empirical analysis is limited to data from the province of Manitoba, Canada, and other regions may show different results.\n\n\nPractical implications\nThis research is useful from a risk management perspective for insurers and reinsurers, and the framework may also be used to develop improved weather risk management strategies to help manage adverse weather events.\n\n\nOriginality/value\nThis is the first paper to integrate a credibility estimator for crop yield forecasting, and develop a closed form reinsurance pricing formula.\n","PeriodicalId":402954,"journal":{"name":"FoodSciRN: Other Agricultural Food Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"A Credibility-Based Yield Forecasting Model for Crop Reinsurance Pricing and Weather Risk Management\",\"authors\":\"Wenjun Zhu, Lysa Porth, K. S. Tan\",\"doi\":\"10.2139/ssrn.2663932\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nThe purpose of this paper is to propose an improved reinsurance pricing framework, which includes a crop yield forecasting model that integrates weather variables and crop production information from different geographically correlated regions using a new credibility estimator, and closed form reinsurance pricing formulas. A yield restatement approach to account for changing crop mix through time is also demonstrated.\\n\\n\\nDesign/methodology/approach\\nThe new crop yield forecasting model is empirically analyzed based on detailed farm-level data from Manitoba, Canada, covering 216 crop varieties from 19,238 farms from 1996 to 2011. As well, corresponding weather data from 30 stations, including daily temperature and precipitation, are considered. Algorithms that combine screening regression, cross-validation and principal component analysis are evaluated for the purpose of achieving efficient dimension reduction and model selection.\\n\\n\\nFindings\\nThe results show that the new yield forecasting model provides significant improvements over the classical regression model, both in terms of in-sample and out-of-sample forecasting abilities.\\n\\n\\nResearch limitations/implications\\nThe empirical analysis is limited to data from the province of Manitoba, Canada, and other regions may show different results.\\n\\n\\nPractical implications\\nThis research is useful from a risk management perspective for insurers and reinsurers, and the framework may also be used to develop improved weather risk management strategies to help manage adverse weather events.\\n\\n\\nOriginality/value\\nThis is the first paper to integrate a credibility estimator for crop yield forecasting, and develop a closed form reinsurance pricing formula.\\n\",\"PeriodicalId\":402954,\"journal\":{\"name\":\"FoodSciRN: Other Agricultural Food Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-02-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"FoodSciRN: Other Agricultural Food Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.2663932\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"FoodSciRN: Other Agricultural Food Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.2663932","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

摘要

目的提出一种改进的再保险定价框架,该框架包括一个作物产量预测模型,该模型使用一种新的可信度估计器整合了不同地理相关区域的天气变量和作物生产信息,以及封闭形式的再保险定价公式。还演示了一种产量重述方法,以说明随着时间的推移作物组合的变化。设计/方法/方法基于加拿大马尼托巴省1996 - 2011年间19238个农场的216个作物品种的详细农场数据,对新的作物产量预测模型进行了实证分析。此外,还考虑了30个站点的相应天气数据,包括日气温和降水。结合筛选回归、交叉验证和主成分分析的算法进行评估,以实现有效的降维和模型选择。结果表明,与传统回归模型相比,该模型在样本内和样本外的预测能力均有显著提高。本实证分析仅限于加拿大马尼托巴省的数据,其他地区可能会显示不同的结果。实际意义从风险管理的角度来看,本研究对保险公司和再保险公司是有用的,该框架也可用于制定改进的天气风险管理策略,以帮助管理不利天气事件。原创性/价值本文首次将可信度估计量整合到作物产量预测中,并开发了封闭形式的再保险定价公式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Credibility-Based Yield Forecasting Model for Crop Reinsurance Pricing and Weather Risk Management
Purpose The purpose of this paper is to propose an improved reinsurance pricing framework, which includes a crop yield forecasting model that integrates weather variables and crop production information from different geographically correlated regions using a new credibility estimator, and closed form reinsurance pricing formulas. A yield restatement approach to account for changing crop mix through time is also demonstrated. Design/methodology/approach The new crop yield forecasting model is empirically analyzed based on detailed farm-level data from Manitoba, Canada, covering 216 crop varieties from 19,238 farms from 1996 to 2011. As well, corresponding weather data from 30 stations, including daily temperature and precipitation, are considered. Algorithms that combine screening regression, cross-validation and principal component analysis are evaluated for the purpose of achieving efficient dimension reduction and model selection. Findings The results show that the new yield forecasting model provides significant improvements over the classical regression model, both in terms of in-sample and out-of-sample forecasting abilities. Research limitations/implications The empirical analysis is limited to data from the province of Manitoba, Canada, and other regions may show different results. Practical implications This research is useful from a risk management perspective for insurers and reinsurers, and the framework may also be used to develop improved weather risk management strategies to help manage adverse weather events. Originality/value This is the first paper to integrate a credibility estimator for crop yield forecasting, and develop a closed form reinsurance pricing formula.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信