The effects of weather on agriculture in recent years have become a major
global concern. Hence, the need for an effective weather risk management tool
(i.e., weather derivatives) that can hedge crop yields against weather
uncertainties. However, most smallholder farmers and agricultural stakeholders
are unwilling to pay for the price of weather derivatives (WD) because of the
presence of basis risks (product-design and geographical) in the pricing
models. To eliminate product-design basis risks, a machine learning ensemble
technique was used to determine the relationship between maize yield and
weather variables. The results revealed that the most significant weather
variable that affected the yield of maize was average temperature. A
mean-reverting model with a time-varying speed of mean reversion, seasonal
mean, and local volatility that depended on the local average temperature was
then proposed. The model was extended to a multi-dimensional model for
different but correlated locations. Based on these average temperature models,
pricing models for futures, options on futures, and basket futures for
cumulative average temperature and growing degree-days are presented. Pricing
futures on baskets reduces geographical basis risk, as buyers have the
opportunity to select the most appropriate weather stations with their desired
weight preference. With these pricing models, farmers and agricultural
stakeholders can hedge their crops against the perils of extreme weather.Comment: 28 pages, 6 figure
