Modelling spatiotemporal variability of temperature

Xiaofeng Cao, Ostap Okhrin, Martin Odening and Matthias Ritter

No 2014-020, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

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.

Keywords: weather insurance; semiparametric model; factor model; Kriging; geographic basis risk (search for similar items in EconPapers)
JEL-codes: C14 C53 G32 (search for similar items in EconPapers)
Date: 2014
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Journal Article: Modelling spatio-temporal variability of temperature (2015)
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