A Credibility-Based Yield Forecasting Model for Crop Reinsurance Pricing and Weather Risk Management

Abstract

The agriculture sector relies on insurance and reinsurance as a mechanism to spread loss. Possible changes in climate, such as an increase in the frequency and severity of spatially correlated weather events, may lead to increased insurance costs. In some cases the structure of risk-sharing arrangements between governments and the private sector, which have historically proven important in the successful delivery of crop insurance programs in many countries, may also be impacted. This paper proposes a new reinsurance pricing framework, including a new 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. The model is empirically analyzed, with an original comprehensive weather index system, and algorithms that combine screening regression (SR), cross validation (CV) and principal component analysis (PCA) to achieve efficient dimension reduction and model selection. The results show that the forecasting model has significantly improved the classical regression model, in term of both in-sample and out-of-sample forecasting abilities. Based on this framework, agricultural insurers and reinsurers may also develop improved weather risk management strategies to help manage adverse weather events.