IDEAS home Printed from https://ideas.repec.org/a/taf/quantf/v12y2012i3p489-500.html
   My bibliography  Save this article

Temperature models for pricing weather derivatives

Author

Listed:
  • Frank Schiller
  • Gerold Seidler
  • Maximilian Wimmer

Abstract

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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/14697681003777097
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:quantf:v:12:y:2012:i:3:p:489-500. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Chris Longhurst). General contact details of provider: http://www.tandfonline.com/RQUF20 .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.