Scholarlink

<p>When crop insurance programs are introduced in areas with limited historical data, premium rates are often based on proxy data from neighboring regions. This study evaluates the credibility of such proxy data in rating US county-level cotton revenue insurance contracts. We apply flexible copula-based models and value at risk to estimate target (‘true’) premiums using complete yield and price data, and simulate missing completely at random scenarios where proxy data are used to fill gaps. Premiums based on pooled proxy data from two or more adjacent counties are then compared to target rates using root mean square error and diversification effects. Results reveal substantial (−37 to +189%) distortion in proxy-based premiums across policy types, with each policy variant exhibiting a unique optimal proxy. We identify alternative risk pools and copula-specific models that improve premium accuracy by up to 50%, saving $9.8 million in subsidies in Texas alone. These findings underscore the importance of robust proxy data selection to minimize market distortions from inaccurate premiums and subsidies, and enhance the sustainability of crop insurance programs.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Modeling Credibility of Area-level Crop Insurance Under Proxy Yields with Non-Gaussian Copulas

  • 期刊论文

  • 发表日期：

    2026年4月28日

查看全文
  • Sebastain N. Awondo

摘要

When crop insurance programs are introduced in areas with limited historical data, premium rates are often based on proxy data from neighboring regions. This study evaluates the credibility of such proxy data in rating US county-level cotton revenue insurance contracts. We apply flexible copula-based models and value at risk to estimate target (‘true’) premiums using complete yield and price data, and simulate missing completely at random scenarios where proxy data are used to fill gaps. Premiums based on pooled proxy data from two or more adjacent counties are then compared to target rates using root mean square error and diversification effects. Results reveal substantial (−37 to +189%) distortion in proxy-based premiums across policy types, with each policy variant exhibiting a unique optimal proxy. We identify alternative risk pools and copula-specific models that improve premium accuracy by up to 50%, saving $9.8 million in subsidies in Texas alone. These findings underscore the importance of robust proxy data selection to minimize market distortions from inaccurate premiums and subsidies, and enhance the sustainability of crop insurance programs.