Weather forecasting for weather derivatives

• 成果类型: Article
• 署名作者: Campbell, SD; Diebold, FX
• 署名单位: Brown University; University of Pennsylvania
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000001051
• 发表日期: 2005
• 页码: 6-16
• 关键词: climatological time-series; returns; models

摘要

We take a simple time series approach to modeling and forecasting daily average temperature in U.S. cities, and we inquire systematically weather derivatives market. The answer is, perhaps supris- ingly to whether it may prove useful from the vantage point of participants in the ingly, yes. Time series modeling reveals conditional mean dynamics and, crucially, strong conditional variance dynamics in daily average temperature, and it reveals sharp differences between the distribution of temperature and the distribution of temperature surprises. As we argue, it also holds promise for producing the long-horizon predictive densities crucial for pricing weather derivatives, so that additional inquiry into time series weather forecasting methods will likely prove useful in weather derivatives contexts.