Working Paper: NBER ID: w10141
Authors: Sean D. Campbell; Francis X. Diebold
Abstract: We take a simple time-series approach to modeling and forecasting daily average temperature in U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes. Time-series modeling reveals both strong conditional mean dynamics and conditional variance dynamics in daily average temperature, and it reveals sharp differences between the distribution of temperature and the distribution of temperature surprises. The approach can easily be used to produce not only short-horizon point forecasts, but also the long-horizon density forecasts of maximal relevance in weather derivatives contexts. We produce and evaluate both, with some success. We conclude that additional inquiry into nonstructural weather forecasting methods will likely prove useful in weather derivatives contexts.
Keywords: weather derivatives; forecasting; time series; temperature modeling
JEL Codes: G1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| time series modeling (C22) | strong conditional mean dynamics (C69) |
| time series modeling (C22) | significant conditional variance dynamics (C32) |
| conditional variance dynamics (C69) | volatility in temperature forecasting (Q47) |
| distribution of temperature (D39) | distribution of temperature surprises (C46) |
| time series approach (C22) | short-horizon point forecasts (G17) |
| time series approach (C22) | long-horizon density forecasts (C53) |