Research On Multi-crop Joint Income Insurance And Its Pricing

Agriculture is the primary industry in China,which is a major agricultural country.Agricultural production is subject to complex yield and price risks.Agricultural insurance is an important tool to protect agricultural production risks,and crop income insurance is an innovative type of agricultural insurance that covers both yield and price risks,and can better help farmers to cope with the impact of natural disasters,climate change and market fluctuations on their income.At present,crop income insurance in China is mainly single-crop income insurance.Multiple crops are often grown in the same area,and prices and yields of multiple crops are dependent on each other due to regional climatic and economic influences.Single crop income insurance ignores the interactions between crops and considers the correlation between price and yield of only one crop,which can lead to biased rate setting.Therefore,it is important to conduct in-depth research on multi-crop joint income insurance and its pricing in order to improve the scientificity of agricultural insurance rate setting and enhance the risk protection capacity of agricultural insurance.In this paper,we use the innovative multivariate dependency modeling method of mutual information Vine Copula structure learning to construct a high-dimensional dependency risk model for yield and price of three major crops in Liaoning Province,and on this basis,we calculate the rates of multi-crop joint income insurance.The results show that the combined income insurance premiums are lower than the combined single crop income insurance premiums for the three crops.In the case of multiple crops,there is a problem of double payment of premiums for single crop income insurance.The research conducted in this paper is of great relevance and long-term significance in improving the rationality and accuracy of crop income insurance pricing results,promoting diversification and sustainable development of agricultural insurance,and facilitating rural economic development.