Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/93216
Authors: 
Cao, Xiaofeng
Okhrin, Ostap
Odening, Martin
Ritter, Matthias
Year of Publication: 
2014
Series/Report no.: 
SFB 649 Discussion Paper 2014-020
Abstract: 
Forecasting temperature in time and space is an important precondition for both the design of weather derivatives and the assessment of the hedging effectiveness of index based weather insur-ance. In this article, we show how this task can be accomplished by means of Kriging techniques. Moreover, we compare Kriging with a dynamic semiparametric factor model (DSFM) that has been recently developed for the analysis of high dimensional financial data. We apply both methods to comprehensive temperature data covering a large area of China and assess their performance in terms of predicting a temperature index at an unobserved location. The results show that the DSFM performs worse than standard Kriging techniques. Moreover, we show how geographic basis risk inherent to weather derivatives can be mitigated by regional diversification.
Subjects: 
weather insurance
semiparametric model
factor model
Kriging
geographic basis risk
JEL: 
C14
C53
G32
Document Type: 
Working Paper

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