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**Modelling spatio-temporal variability of temperature.**
*(English)*
Zbl 1342.65022

Summary: 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.

### MSC:

65C60 | Computational problems in statistics (MSC2010) |

62M20 | Inference from stochastic processes and prediction |

62P20 | Applications of statistics to economics |

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\textit{X. Cao} et al., Comput. Stat. 30, No. 3, 745--766 (2015; Zbl 1342.65022)

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