Weather derivatives are usually priced by analyzing the climatologic data of an underlying weather index. This research proved that when using heating and cooling degree days as an underlying weather index that climatology was not fully representative of future outcomes. Previous research attempted to develop techniques for daily mean temperature simulations, but these techniques were based on invalid statistical assumptions and lacked time dependencies in the residuals. Due to the fact that heating and cooling and degree days are an aggregate monthly metric and path dependent, it was important to simulate the complete behavior of a time series.
This research provided an in depth statistical analysis of the daily mean temperature time series for eighteen cities from the Chicago Mercantile Exchange (CME). The residuals were used to develop two models to simulate a possible temperature time series for 2007. A distribution of ten thousand possible outcomes were created for each model, and then analyzed against the climatologic data sets. Ultimately, this research exhibited that techniques involving daily mean temperature simulation from the statistical analysis of the residuals could accurately produce likely outcomes of degree days for weather derivative contracts to be based upon.