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Pricing Weather Derivatives

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  • Dwight R. Sanders
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    Abstract

    This article presents a general method for pricing weather derivatives. Specification tests find that a temperature series for Fresno, CA follows a mean-reverting Brownian motion process with discrete jumps and autoregressive conditional heteroscedastic errors. Based on this process, we define an equilibrium pricing model for cooling degree day weather options. Comparing option prices estimated with three methods: a traditional burn-rate approach, a Black-Scholes-Merton approximation, and an equilibrium Monte Carlo simulation reveals significant differences. Equilibrium prices are preferred on theoretical grounds, so are used to demonstrate the usefulness of weather derivatives as risk management tools for California specialty crop growers. Copyright 2004, Oxford University Press.

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    File URL: http://hdl.handle.net/10.1111/j.0002-9092.2004.00649.x
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    Bibliographic Info

    Article provided by Agricultural and Applied Economics Association in its journal American Journal of Agricultural Economics.

    Volume (Year): 86 (2004)
    Issue (Month): 4 ()
    Pages: 1005-1017

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    Handle: RePEc:oup:ajagec:v:86:y:2004:i:4:p:1005-1017

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    Cited by:
    1. Raimova, Gulnora, 2011. "Variance reduction methods at the pricing of weather options," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 21(1), pages 3-15.
    2. Turvey, Calum G. & Norton, Michael T., 2008. "An Internet-Based Tool for Weather Risk Management," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 37(1), April.
    3. Rong Kong & Calum G. Turvey & Guangwen He & Jiujie Ma & Patrick Meagher, 2011. "Factors influencing Shaanxi and Gansu farmers' willingness to purchase weather insurance," China Agricultural Economic Review, Emerald Group Publishing, vol. 3(4), pages 423-440, November.
    4. repec:ags:nc2006:133091 is not listed on IDEAS
    5. Leif Erec Heimfarth & Oliver Musshoff, 2011. "Weather index-based insurances for farmers in the North China Plain: An analysis of risk reduction potential and basis risk," Agricultural Finance Review, Emerald Group Publishing, vol. 71(2), pages 218-239, July.
    6. Musshoff, Oliver & Odening, Martin & Xu, Wei, 2005. "Zur Reduzierung niederschlagsbedingter Produktionsrisiken mit Wetterderivaten," Working Paper Series 18822, Humboldt University Berlin, Department Agricultural Economics.
    7. Lee, Yongheon & Oren, Shmuel S., 2009. "An equilibrium pricing model for weather derivatives in a multi-commodity setting," Energy Economics, Elsevier, vol. 31(5), pages 702-713, September.
    8. 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, January.
    9. Javier Orlando Pantoja Robayo & Andrea Roncoroni, 2012. "Optimal Static Hedging of Energy Price and Volume Risk: Closed-Form Results," DOCUMENTOS DE TRABAJO CIEF 010668, UNIVERSIDAD EAFIT.
    10. Nadolnyak, Denis A. & Hartarska, Valentina M., 2009. "Weather, Climate, and Agricultural Disaster Payments in the Southeastern U.S," 2009 Conference, August 16-22, 2009, Beijing, China 51802, International Association of Agricultural Economists.
    11. A. Alexandridis & A. Zapranis, 2013. "Wind Derivatives: Modeling and Pricing," Computational Economics, Society for Computational Economics, vol. 41(3), pages 299-326, March.
    12. Andreas Groll & Brenda López-Cabrera & Thilo Meyer-Brandis, 2014. "A consistent two-factor model for pricing temperature derivatives," SFB 649 Discussion Papers SFB649DP2014-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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