The focus in this study is on estimating the underlying weather index for pricing financial derivatives to hedge weather risks in crop production. Different index estimation methods for growing degree days (GDDs) are compared. In particular, daily average temperatures for deriving GDDs are simulated using an econometric model and a stochastic process that uses three methods to estimate the mean-reversion parameter. Finally, the historical approach based on a five-year moving average of GDDs is compared with the econometric and stochastic models. Results indicate that econometric model provides the best fit, followed by the the historical average method and then the stochastic process with a high mean reversion parameter. Premiums from the econometric model with sine function and historical average approaches are closer to those based on realized weather values compared with the stochastic approach. Therefore, the econometric model with sine function and the historical average approach provide better pricing estimates than other methods.