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Indifference Pricing of Weather Derivatives

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Abstract

Weather derivatives are difficult to price due to the nontradability of weather and the absence of liquid secondary markets for these contracts. We use the concept of indifference pricing to develop a model for calculating the willingness to pay for weather insurance. Compared with other approaches, indifference pricing is less ambitious since it does not attempt to predict a transacted market price. The application of indifference pricing in the case of German crop producers shows that their willingness to pay for weather insurance depends on the production program and varies regionally. This suggests the development of tailored insurance products. Copyright 2008, Oxford University Press.

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  • Oliver Musshoff, 2008. "Indifference Pricing of Weather Derivatives," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(4), pages 979-993.
    • Handle: RePEc:oup:ajagec:v:90:y:2008:i:4:p:979-993
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    File URL: http://hdl.handle.net/10.1111/j.1467-8276.2008.01154.x
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    Cited by:

    1. Zhiwei Shen & Martin Odening, 2013. "Coping with systemic risk in index-based crop insurance," Agricultural Economics, International Association of Agricultural Economists, vol. 44(1), pages 1-13, January.
    2. 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.
    3. Bokusheva, Raushan, 2010. "Measuring the dependence structure between yield and weather variables," MPRA Paper 22786, University Library of Munich, Germany.
    4. Truong, Chi & Trück, Stefan & Mathew, Supriya, 2018. "Managing risks from climate impacted hazards – The value of investment flexibility under uncertainty," European Journal of Operational Research, Elsevier, vol. 269(1), pages 132-145.
    5. A. Alexandridis & A. Zapranis, 2013. "Wind Derivatives: Modeling and Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 41(3), pages 299-326, March.
    6. Raushan Bokusheva, 2011. "Measuring dependence in joint distributions of yield and weather variables," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 71(1), pages 120-141, May.
    7. 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.
    8. Hennessy, David A., 2011. "Modeling Stochastic Crop Yield Expectations with a Limiting Beta Distribution," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 36(1), pages 1-15, April.
    9. Ming Pu & Gang-Zhi Fan & Seow Ong, 2012. "Heterogeneous Agents and the Indifference Pricing of Property Index Linked Swaps," The Journal of Real Estate Finance and Economics, Springer, vol. 44(4), pages 543-569, May.
    10. Heng Xiong & Rogemar Mamon, 2018. "Putting a price tag on temperature," Computational Management Science, Springer, vol. 15(2), pages 259-296, June.
    11. Eltazarov, Sarvarbek & Bobojonov, Ihtiyor & Kuhn, Lena & Glauben, Thomas, 2021. "Mapping weather risk – A multi-indicator analysis of satellite-based weather data for agricultural index insurance development in semi-arid and arid zones of Central Asia," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 23.

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