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Financial weather derivatives for corn production in Northern China: A comparison of pricing methods

Author

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  • Sun, Baojing
  • van Kooten, G. Cornelis

Abstract

The focus in this study is on the pricing of financial derivatives for hedging weather risks in crop production. Employing data from an earlier study, we compare different methods for pricing weather derivative options based on growing degree days (GDDs). We employ average daily temperatures to derive GDDs using three approaches: (1) An econometric approach with a sine function; (2) Monte Carlo simulation with a sine function and three methods to estimate the mean-reversion parameter; and (3) a historic approach (burn analysis) based on a 10-year moving average of GDDs. Results indicate that the historical average method provides the best fit, followed by the stochastic process with a high mean reversion speed, and, finally, the approach using the econometrically estimated sine function. Depending on the method used, premiums for weather derivative options vary from $21.27 to $24.39 per GDD index contract.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:empfin:v:32:y:2015:i:c:p:201-209
    DOI: 10.1016/j.jempfin.2015.03.014
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    References listed on IDEAS

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    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.
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    4. 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(3), pages 1-17, December.
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    7. Hung‐Hsi Huang & Yung‐Ming Shiu & Pei‐Syun Lin, 2008. "HDD and CDD option pricing with market price of weather risk for Taiwan," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 28(8), pages 790-814, August.
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    9. Turvey, Calum G. & Kong, Rong & Belltawn, Burgen, 2009. "Weather Risk and the Viability of Weather Insurance In Western China," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49362, Agricultural and Applied Economics Association.
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    Cited by:

    1. 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.
    2. Ai-Ju Shao & Tai-Yi Yu, 2022. "Spatial delineation approach to weather derivatives with three multivariate manners," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(2), pages 1227-1245, January.

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    More about this item

    Keywords

    Agricultural finance; Stochastic processes; Pricing weather options; Growing degree days for corn production;
    All these keywords.

    JEL classification:

    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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