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Modelling spatio-temporal variability of temperature

Author

Listed:
  • Xiaofeng Cao
  • Ostap Okhrin
  • Martin Odening
  • Matthias Ritter

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

Suggested Citation

  • Xiaofeng Cao & Ostap Okhrin & Martin Odening & Matthias Ritter, 2015. "Modelling spatio-temporal variability of temperature," Computational Statistics, Springer, vol. 30(3), pages 745-766, September.
    • Handle: RePEc:spr:compst:v:30:y:2015:i:3:p:745-766
    DOI: 10.1007/s00180-015-0561-2
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    Cited by:

    1. Monbet, Valérie & Ailliot, Pierre, 2017. "Sparse vector Markov switching autoregressive models. Application to multivariate time series of temperature," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 40-51.
    2. Ostap Okhrin & Stefan Trück, 2015. "Editorial to the special issue on Applicable semiparametrics of computational statistics," Computational Statistics, Springer, vol. 30(3), pages 641-646, September.

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    More about this item

    Keywords

    Semiparametric model; Factor model; Kriging; Weather insurance; Geographic basis risk; C14; C53; G32;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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