Weather derivatives based on heating degree days or cooling degree days have been traded in financial markets for more than 10 years. Although used by the energy sector, agricultural producers have been slow to adopt this technology even though agriculture is particularly vulnerable to weather uncertainty. In agriculture, few studies have focused on the pricing of weather derivatives for hedging weather risks for crop production. In this study, we employ data from an earlier study of climate on corn yields in northern China to compare different methods for pricing weather options based on growing degree days (GDDs). For pricing weather options, we investigate the use of weather indexes based on an econometric approach, a mean reverting stochastic process, and simple historical averages (burn analysis). For the econometric model, we use a sine function to estimate expected GDDs. The stochastic model is also based on the sine function, but employs Monte Carlo simulation with mean-reversion parameters to predict daily average temperatures; the reversion parameters are estimated using three alternative methods. For the historical approach, a 10-year moving average of GDDs is used. Results for the period 2001-2011 indicate that the historical average method fits actual GDDs best, followed in order by the stochastic process with a high mean reversion speed (0.9763), the econometrically estimated sine function, and the stochastic processes with medium (0.2698) and low (0.02399) mean reversion speeds. Depending on the method used, premiums for weather derivative options vary from $21.27 to $24.39 per standard deviation in GDD.
