It is a matter of common knowledge that weather represents the major source of uncertainty in crop production. It is to be expected that weather fluctuations will increase in the future due to climate change. Traditionally, farmers tried to protect themselves against weather-related yield variations by buying insurances. More recently, there has been a discussion regarding the use of weather derivatives to safeguard against volumetric risks. Although weather derivatives display advantages over traditional insurances, there is only a relatively small market for these products in agriculture. This is partly attributed to the fact that it is unclear whether and to what extent weather derivatives are a useful instrument of risk management in agriculture. This study applies real yield and weather data from Northeast Germany in order to quantify the risk-reducing effect that can be achieved in wheat production by using precipitation options. To do so stochastic simulation is used. The hedging effectiveness is controlled by the contract design (index, strike level, tick size). However, the local basis risk and the geographical basis risk remain with the farmer. We separate both causes of basis risk and reveal the extent of each. This enables conclusions regarding the design of weather derivatives; thus the question dealt with here is relevant both for farmers and for potential sellers of weather derivatives.

This study examines rainfall variability and its implications for wheat production risk in northeast Germany. The hedging effectiveness of rainfall options and the role of geographical basis risk are analyzed using a daily precipitation model. Simpler pricing methods such as the burn analysis and the index value simulation serve as benchmarks for comparison. It is found that the choice of statistical approach may lead to considerable differences in the estimation results. Daily precipitation models should be used with some caution in the context of derivative pricing because they tend to underestimate rainfall variability. This is unexpected, because daily simulation models are usually preferred in the context of temperature‐based weather indexes.

Purpose: – The purpose of this paper is to review some challenges of insuring weather risk in agriculture and to discuss potential remedies for these problems. Design/methodology/approach: – The paper is developed as a narrative on weather insurance based largely on existing literature. Findings: – Weather risks show characteristics that often violate classical requirements for insurability. First, some weather risks, particularly slowly emerging weather perils like drought, are spatially correlated and cause systemic risks. Second, climatic change may increase the volatility of weather variables and lead to non-stationary loss distributions, which causes difficulties in actuarial ratemaking. Third, limited availability of yield and weather data hinders the estimation of reliable loss distributions. Practical implications: – Some of the approaches discussed in this review, such as time diversification, local test procedures and the augmentation of observational data by expert knowledge, can be useful for crop insurance companies to improve their risk management and product design. Originality/value: – This study provides background and development information regarding weather insurance and also presents statistical tools and actuarial methods that support the assessment of weather risks as well as the design of weather and yield insurance products.

Purpose – The purpose of this paper is to assess the losses of weather-related insurance at different regional levels. The possibility of spatial diversification of insurance is explored by estimating the joint occurrence on unfavorable weather conditions in different locations, looking particularly at the tail behavior of the loss distribution. Design/methodology/approach – Joint weather-related losses are estimated using copulas. Copulas avoid the direct estimation of multivariate distributions but allow for much greater flexibility in modeling the dependence structure of weather risks compared with simple correlation coefficients. Findings – Results indicate that indemnity payments based on temperature as well as on cumulative rainfall show strong stochastic dependence even at a large regional scale. Thus the possibility to reduce risk exposure by increasing the trading area of insurance is limited. Research limitations/implications – The empirical findings are limited by a rather weak database. In that case the estimation of high-dimensional copulas leads to large estimation errors. Practical implications – The paper includes implications for the quantification of systemic weather risk which is important for the rate making of crop insurance and reinsurance. Originality/value – This paper's results highlight how important the choice of the statistical approach is when modeling the dependence structure of weather risks.