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Temperature models for pricing weather derivatives


  • Frank Schiller
  • Gerold Seidler
  • Maximilian Wimmer


We present four models for predicting temperatures that can be used for pricing weather derivatives. Three of the models have been suggested in previous literature, and we propose another model that uses splines to remove trend and seasonality effects from temperature time series in a flexible way. Using historical temperature data from 35 weather stations across the United States, we test the performance of the models by evaluating virtual heating degree days (HDD) and cooling degree days (CDD) contracts. We find that all models perform better when predicting HDD indices than predicting CDD indices. However, all models based on a daily simulation approach significantly underestimate the variance of the errors.

Suggested Citation

  • Frank Schiller & Gerold Seidler & Maximilian Wimmer, 2012. "Temperature models for pricing weather derivatives," Quantitative Finance, Taylor & Francis Journals, vol. 12(3), pages 489-500, March.
  • Handle: RePEc:taf:quantf:v:12:y:2012:i:3:p:489-500
    DOI: 10.1080/14697681003777097

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    Cited by:

    1. Lu Zong & Manuela Ender, 2016. "Spatially-Aggregated Temperature Derivatives: Agricultural Risk Management in China," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 4(3), pages 1-17, September.
    2. Ahmet Göncü, 2013. "Comparison of temperature models using heating and cooling degree days futures," Journal of Risk Finance, Emerald Group Publishing, vol. 14(2), pages 159-178, February.
    3. Sun, Baojing & van Kooten, G. Cornelis, 2015. "Financial weather derivatives for corn production in Northern China: A comparison of pricing methods," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 201-209.
    4. Gülpınar, Nalân & Çanakoḡlu, Ethem, 2017. "Robust portfolio selection problem under temperature uncertainty," European Journal of Operational Research, Elsevier, vol. 256(2), pages 500-523.

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