IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

Minimizing geographical basis risk of weather derivatives using a multi-site rainfall model

  • Ritter, Matthias
  • Musshoff, Oliver
  • Odening, Martin

Weather risk is one of the main causes for income fluctuation in agriculture. Since 1997, the economic consequences of weather risk can be insured with weather derivatives, which are offered for many different weather events, such as temperature, rainfall, snow or hurricanes. It is well known that the hedging effectiveness of weather derivatives is interfered by the existence of geographical basis risk, i.e., the deviation of weather conditions at different locations. In this paper, we explore how geographical basis risk of rainfall based derivatives can be reduced by regional diversification. Minimizing geographical basis risk requires knowledge of the joint distribution of rainfall at different locations. For that purpose, we estimate a daily multi-site rainfall model from which optimal portfolio weights are derived. We find that this method allows to reduce geographical basis risk more efficiently than simpler approaches as, for example, inverse distance weighting.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://purl.umn.edu/122527
Download Restriction: no

Paper provided by European Association of Agricultural Economists in its series 123rd Seminar, February 23-24, 2012, Dublin, Ireland with number 122527.

as
in new window

Length:
Date of creation: 23 Feb 2012
Date of revision:
Handle: RePEc:ags:eaa123:122527
Contact details of provider: Web page: http://www.eaae.org
Email:


More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-44, January.
  2. Yufei Jin & Roderick Rejesus & Bertis Little, 2005. "Binary choice models for rare events data: a crop insurance fraud application," Applied Economics, Taylor & Francis Journals, vol. 37(7), pages 841-848.
  3. Andrew Ang & Joseph Chen & Yuhang Xing, 2005. "Downside risk," Proceedings, Board of Governors of the Federal Reserve System (U.S.).
  4. Martin Odening & Oliver Musshoff & Wei Xu, 2007. "Analysis of rainfall derivatives using daily precipitation models: opportunities and pitfalls," Agricultural Finance Review, Emerald Group Publishing, vol. 67(1), pages 135-156, May.
  5. Ernst Berg & Bernhard Schmitz, 2008. "Weather-based instruments in the context of whole-farm risk management," Agricultural Finance Review, Emerald Group Publishing, vol. 68(1), pages 119-133, September.
  6. Xu, Wei & Filler, Gunther & Odening, Martin & Okhrin, Ostap, 2009. "On the Systemic Nature of Weather Risk," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49131, Agricultural and Applied Economics Association.
  7. López Cabrera, Brenda & Odening, Martin & Ritter, Matthias, 2013. "Pricing rainfall futures at the CME," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4286-4298.
  8. Oliver Musshoff & Martin Odening & Wei Xu, 2009. "Management of climate risks in agriculture-will weather derivatives permeate?," Applied Economics, Taylor & Francis Journals, vol. 43(9), pages 1067-1077.
  9. Wolfgang Härdle & Maria Osipenko, 2011. "Pricing Chinese rain: a multi-site multi-period equilibrium pricing model for rainfall derivatives," SFB 649 Discussion Papers SFB649DP2011-055, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  10. Jason D. Woodard & Philip Garcia, 2008. "Basis risk and weather hedging effectiveness," Agricultural Finance Review, Emerald Group Publishing, vol. 68(1), pages 99-117, September.
  11. Vedenov, Dmitry V. & Barnett, Barry J., 2004. "Efficiency of Weather Derivatives as Primary Crop Insurance Instruments," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 29(03), December.
  12. J. P. Hughes & P Guttorp & S. P. Charles, 1999. "A non-homogeneous hidden Markov model for precipitation occurrence," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(1), pages 15-30.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:ags:eaa123:122527. 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: (AgEcon Search)

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.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 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.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.