Modelling spatio-temporal variability of temperature

Xiaofeng Cao, Ostap Okhrin, Martin Odening and Matthias Ritter

Computational Statistics, 2015, vol. 30, issue 3, 745-766

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 insurance. 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. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Semiparametric model; Factor model; Kriging; Weather insurance; Geographic basis risk; C14; C53; G32 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (2)

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Working Paper: Modelling spatiotemporal variability of temperature (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:30:y:2015:i:3:p:745-766

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DOI: 10.1007/s00180-015-0561-2

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