Hedging the Price Risk Inherent in Revenue Protection Insurance

Crop revenue insurance is unique, because it involves a guarantee subsuming yield risk and highly systematic price risk. This study examines whether crop insurers could use options instead of, or in addition to, assigning policies to the Commercial Funds of the USDA Federal Crop Insurance Corporation (FCIC) as per the Standard Reinsurance Agreement (SRA) to hedge the price risk of revenue insurance policies. The behavioral model examines the optimal hedge ratio for a crop insurer with a book of business consisting of corn Revenue Protection (RP) policies. Results show that a mix of put and call options can hedge the price risk of the RP policies. The higher optimal hedge ratios of call options as compared to put options imply that the risk of increased liability due to upside price risk can be hedged using options better than downside price risk. This study also analyzed the combination of options with the SRA at 35, 50, and 75% retention levels. The zero optimal hedge ratios at each retention level and the negative correlation between RP indemnities and the option returns when the crop insurer mixed options and SRA suggest that the purchasing of options provides no additional risk protection to crop insurers beyond what is provided by the SRA despite retention limits.

The effects of overconfident yield forecasting on cross-coverage level crop insurance demand

PurposeThis paper examines the effect of overconfident yield forecasting (optimism bias) on crop insurance coverage level choices across both yield and revenue insurance.Design/methodology/approachThis study simulates a representative producer’s preferred coverage level for both yield and revenue insurance under three potential models of decision-making and four potential manifestations of overconfident yield forecasting. The study then uses this framework to examine how coverage level choices change as overconfidence increases (decreases).FindingsAs overconfidence increases, producers prefer lower levels of crop insurance coverage than they would otherwise prefer, with extreme overconfidence inducing farmers to buy no insurance at all. While overconfidence affects cross-coverage demand for revenue and yield insurance similarly, this effect is more pronounced for yield insurance. Cross-coverage level demand for revenue insurance is relatively stable across changes in the correlation between prices and yields.Practical implicationsThis research has important implications for crop insurance subsidy design and crop insurance demand modeling.Originality/valueThere is a growing body of literature suggesting that producers are overconfident with regard to their future yield risk and that this bias reduces their willingness to pay for risk management tools such as crop insurance. This is the first study to look at how such overconfidence affects cross-coverage level demand for crop insurance.

A New Tool for Covering Risk in Agriculture: The Revenue Insurance Policy

Over the last years, the agricultural sector has faced increasing risks related not only to production activities, but also to climate adversity and a higher frequency of extreme events. These factors, combined with increased price volatility in the markets, have caused greater exposure to risk for farmers. For this reason, risk management in agriculture has taken on an important role within the Common Agricultural Policy. However, in recent years, gradual disaffection of farmers, low penetration of insurance in the arable sector, and a greater need for insurance coverage against market risks have characterised the subsidised risk management system. For all these reasons, starting in 2017, the National Agricultural Insurance Plan has provided new possibilities for covering risks. This paper aims to contribute to the debate on risk management linked to the revenue insurance policy recently adopted in Italy. Using data from the Italian Farm Accountancy Data Network, we simulate the application of the revenue insurance policy with a sample of Italian farms operating in the common and durum wheat sectors. The main findings show that the revenue insurance policy stipulation is, overall, sustainable for both farms and insurance companies.

Research on Revenue Insurance Premium Ratemaking of Jujube Based on Copula-Stochastic Optimization Model

During the process of jujube planting, there are not only natural risks caused by natural disasters but also market risks caused by price factors. In the study, firstly, wavelet analysis method was used to stabilize the jujube yield per unit area and the jujube price from 1997 to 2018 in Aksu region, Xinjiang, China. Secondly, EasyFit software was used to fit the distribution functions of yield per unit area and price, respectively. Thirdly, the optimal Copula function which connects the marginal distribution functions and its joint distribution function was selected with the principle of “the minimum square distance from the empirical Copula function.” Finally, taking the premium rate and the insurance amount as two decision variables, the farmer’s risk minimization as the objective function, around the four constraints of functions and role of insurance, the nonspeculative nature of insurance, the sustainability of insurance, and the moral hazard factors and the farmers’ willing to participate in insurance, the Copula-stochastic optimization model was set up to determine the premium rate of jujube revenue insurance in Aksu region.

Whole-Farm Revenue Protection as a Factor of Economic Stability in Crop Production

Crop production is largely unprotected and exposed to a great number of production factors. On the other hand, farmers are exposed to fluctuations in the market prices of their products every year, which often has a negative impact on the profits made. There are various risk management measures in plant production, and insurance is certainly one of the most effective instruments. One of the recent insurance models is Whole-Farm Revenue Insurance (WFRP), which is an American insurance model that has been applied since 2015. The essence of WFRP is to ensure that all crops on the farm are secured against production and market risks with only one policy. The aim of the research in this paper is to present WFRP as an entirely new model of revenue insurance on the example of a typical Serbian farm specializing in crop production. The WFRP model works by determining the insured revenue before the start of the production year. If at the end of the production year, for any reason, the realized revenue falls below the level of insured revenue, the farmer is entitled to indemnification. Due to the drought that hit the region where the analyzed farm is located, the yields were reduced, and thus the expected revenue was also reduced, and the farmer was entitled to damages of $5697. On the other hand, it is the farmer’s obligation to pay $373 to the insurer as a risk transfer fee. The authors proved that even such complex insurance models can be applied in countries such as Serbia, where awareness of the importance of insurance of agricultural production is still not developed.

Rating exotic price coverage in crop revenue insurance

PurposeRevenue insurance is the most popular form of insurance available in the US federal crop insurance program. The majority of crop revenue policies are sold with a harvest price replacement feature that pays out on lost crop yields at the maximum of a realized or projected harvest price. The authors introduce a novel actuarial and statistical approach to rate revenue insurance policies with exotic price coverage: the payout depends on an order statistic or average of prices. The authors examine the price implications of different dependence models and demonstrate the feasibility of policies of this type.Design/methodology/approachHierarchical Archimedean copulas and vine copulas are used to model dependence between prices and yields and serial dependence of prices. The authors construct several synthetic exotic price coverage insurance policies and evaluate the impact of copula models on policies covering different types of risk.FindingsThe authors’ findings show that the price of exotic price coverage policies is sensitive to the choice of dependence model. Serial dependence varies across the growing season. It is possible to accurately price exotic coverage policies and we suggest these add-ons as a possible avenue for developing private crop insurance markets.Originality/valueThe authors apply hierarchical Archimedean copulas and vine copulas that allow for flexibility in the modeling of multivariate dependence. Unlike previous research, which has primarily considered dependence across space, the form of exotic price coverage requires modeling serial dependence in relative prices. Results are important for this segment of the agricultural insurance market: one of the main areas that insurers can develop private products around the federal program.

Risk Management Tools for Sustainable Agriculture: A Model for Calculating the Average Price for the Season in Revenue Insurance for Citrus Fruit

Risk management in agriculture is at the heart of major reforms in many OECD countries and European agricultural policies. Price risks, which are generally not insurable per se, have been covered by the Common Agricultural Policy (CAP), which has been shaped as a system of protection against market shocks and an instrument for income stabilization. However, there is an increasing propensity to combine the use of public and private risk management tools as well. In Spain, revenue insurance has not yet developed in the same way as other risk coverage insurance, although it is an upcoming target of agricultural insurance policies with the aim of ensuring income stability for agricultural producers. This paper presents the results of the methodology used to draw up a composition index or model of the average price for the season or representative market field price to be used for revenue insurance purposes in citrus fruit. High explanatory power regression models and the analytic hierarchy process (AHP) were used. The results show that the average price for the season obtained reliably represents the market field prices in the country’s various producer areas.