Computational Statistics

, Volume 30, Issue 3, pp 745–766 | Cite as

Modelling spatio-temporal variability of temperature

  • Xiaofeng Cao
  • Ostap Okhrin
  • Martin Odening
  • Matthias Ritter
Original Paper

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.

Keywords

Semiparametric model Factor model Kriging Weather insurance Geographic basis risk 

JEL Classification

C14 C53 G32 

Notes

Acknowledgments

Financial support from the German Research Foundation via CRC 649 “Economic Risk” is gratefully acknowledged.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Xiaofeng Cao
    • 1
  • Ostap Okhrin
    • 2
    • 3
  • Martin Odening
    • 1
  • Matthias Ritter
    • 1
  1. 1.Department of Agricultural EconomicsHumboldt-Universität zu BerlinBerlinGermany
  2. 2.Chair of Statistics and Econometrics, Transportation FacultyTechnische Universität DresdenDresdenGermany
  3. 3.SFB 649, School of Business and EconomicsHumboldt-Universität zu BerlinBerlinGermany

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