IDEAS home Printed from https://ideas.repec.org/p/ags/aaea06/21050.html
   My bibliography  Save this paper

Modeling and Hedging Rain Risk

Author

Listed:
  • Musshoff, Oliver
  • Odening, Martin
  • Xu, Wei

Abstract

In this article we price a precipitation option based on empirical weather data from Germany using different pricing methods, among them Burn Analysis, Index Value Simulation and Daily Simulation. For that purpose we develop a daily precipitation model. Moreover, a de-correlation analysis is proposed to assess the spatial basis risk that is inherent to rainfall derivatives. The models are applied to precipitation data in Brandenburg, Germany. Based on simplifying assumptions of the production function, we quantify and compare the risk exposure of grain producers with and without rainfall insurance. It turns out that a considerable risk remains with producers who are remotely located from the weather station. Another finding is that significant differences may occur between the pricing methods. We identify the strengths and weaknesses of the pricing methods and give some recommendations for their applications. Our results are relevant for producers as well as for potential sellers of weather derivatives.

Suggested Citation

  • Musshoff, Oliver & Odening, Martin & Xu, Wei, 2006. "Modeling and Hedging Rain Risk," 2006 Annual meeting, July 23-26, Long Beach, CA 21050, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  • Handle: RePEc:ags:aaea06:21050
    DOI: 10.22004/ag.econ.21050
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/21050/files/sp06mu01.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.21050?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Sean D. Campbell & Francis X. Diebold, 2005. "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 6-16, March.
    2. Skees, Jerry*Gober, Stephanie*Varangis, Panos*Le, 2001. "Developing rainfall-based index insurance in Morocco," Policy Research Working Paper Series 2577, The World Bank.
    3. Richards, Timothy J. & Manfredo, Mark R. & Sanders, Dwight R., 2004. "Pricing Weather Derivatives," Working Papers 28536, Arizona State University, Morrison School of Agribusiness and Resource Management.
    4. Turvey, Calum G., 1999. "The Essentials Of Rainfall Derivatives And Insurance," Working Papers 34149, University of Guelph, Department of Food, Agricultural and Resource Economics.
    5. Peter Alaton & Boualem Djehiche & David Stillberger, 2002. "On modelling and pricing weather derivatives," Applied Mathematical Finance, Taylor & Francis Journals, vol. 9(1), pages 1-20.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Kim, Daeyoung & Kim, Jong-Min & Liao, Shu-Min & Jung, Yoon-Sung, 2013. "Mixture of D-vine copulas for modeling dependence," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 1-19.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fred Benth & Wolfgang Karl Härdle & Brenda López Cabrera, 2009. "Pricing of Asian temperature risk," SFB 649 Discussion Papers SFB649DP2009-046, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    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. Markus Stowasser, 2011. "Modelling rain risk: a multi-order Markov chain model approach," Journal of Risk Finance, Emerald Group Publishing, vol. 13(1), pages 45-60, December.
    4. Prabakaran, Sellamuthu & Garcia, Isabel C. & Mora, Jose U., 2020. "A temperature stochastic model for option pricing and its impacts on the electricity market," Economic Analysis and Policy, Elsevier, vol. 68(C), pages 58-77.
    5. Heng Xiong & Rogemar Mamon, 2018. "Putting a price tag on temperature," Computational Management Science, Springer, vol. 15(2), pages 259-296, June.
    6. Groll, Andreas & López-Cabrera, Brenda & Meyer-Brandis, Thilo, 2016. "A consistent two-factor model for pricing temperature derivatives," Energy Economics, Elsevier, vol. 55(C), pages 112-126.
    7. Turvey, Calum G. & Weersink, Alfons, 2005. "Pricing Weather Insurance with a Random Strike Price: An Application to the Ontario Ice Wine Harvest," 2005 Annual meeting, July 24-27, Providence, RI 19255, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    8. Wei Yuan & Ahmet Göncü & Giray Ökten, 2015. "Estimating sensitivities of temperature-based weather derivatives," Applied Economics, Taylor & Francis Journals, vol. 47(19), pages 1942-1955, April.
    9. Ahmet Göncü, 2011. "Pricing temperature-based weather derivatives in China," Journal of Risk Finance, Emerald Group Publishing, vol. 13(1), pages 32-44, December.
    10. Rui Zhou & Johnny Siu-Hang Li & Jeffrey Pai, 2019. "Pricing temperature derivatives with a filtered historical simulation approach," The European Journal of Finance, Taylor & Francis Journals, vol. 25(15), pages 1462-1484, October.
    11. Musshoff, Oliver & Hirschauer, Norbert, 2008. "Hedging von Mengenrisiken in der Landwirtschaft – Wie teuer dürfen „ineffektive“ Wetterderivate sein?," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 57(05), pages 1-12.
    12. Turvey, Calum G. & Norton, Michael, 2008. "An Internet-Based Tool for Weather Risk Management," Agricultural and Resource Economics Review, Cambridge University Press, vol. 37(1), pages 63-78, April.
    13. Aur'elien Alfonsi & Nerea Vadillo, 2023. "Risk valuation of quanto derivatives on temperature and electricity," Papers 2310.07692, arXiv.org, revised Apr 2024.
    14. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    15. Jr‐Wei Huang & Sharon S. Yang & Chuang‐Chang Chang, 2018. "Modeling temperature behaviors: Application to weather derivative valuation," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(9), pages 1152-1175, September.
    16. Larsson, Karl, 2023. "Parametric heat wave insurance," Journal of Commodity Markets, Elsevier, vol. 31(C).
    17. Monika Wieczorek-Kosmala, 2020. "Weather Risk Management in Energy Sector: The Polish Case," Energies, MDPI, vol. 13(4), pages 1-21, February.
    18. L. Kermiche & N. Vuillermet, 2016. "Weather derivatives structuring and pricing: a sustainable agricultural approach in Africa," Applied Economics, Taylor & Francis Journals, vol. 48(2), pages 165-177, January.
    19. Dupuis, Debbie J., 2011. "Forecasting temperature to price CME temperature derivatives," International Journal of Forecasting, Elsevier, vol. 27(2), pages 602-618.
    20. Helene Hamisultane, 2010. "Utility-based pricing of weather derivatives," The European Journal of Finance, Taylor & Francis Journals, vol. 16(6), pages 503-525.

    More about this item

    Keywords

    Risk and Uncertainty;

    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:ags:aaea06:21050. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.