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Weather Derivatives and Stochastic Modelling of Temperature

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  • Fred Espen Benth
  • Jūratė Šaltytė Benth

Abstract

We propose a continuous-time autoregressive model for the temperature dynamics with volatility being the product of a seasonal function and a stochastic process. We use the Barndorff-Nielsen and Shephard model for the stochastic volatility. The proposed temperature dynamics is flexible enough to model temperature data accurately, and at the same time being analytically tractable. Futures prices for commonly traded contracts at the Chicago Mercantile Exchange on indices like cooling- and heating-degree days and cumulative average temperatures are computed, as well as option prices on them.

Suggested Citation

  • Fred Espen Benth & Jūratė Šaltytė Benth, 2011. "Weather Derivatives and Stochastic Modelling of Temperature," International Journal of Stochastic Analysis, Hindawi, vol. 2011, pages 1-21, July.
    • Handle: RePEc:hin:jnijsa:576791
    DOI: 10.1155/2011/576791
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    Cited by:

    1. Fred Espen Benth & Anca Pircalabu, 2018. "A non-Gaussian Ornstein–Uhlenbeck model for pricing wind power futures," Applied Mathematical Finance, Taylor & Francis Journals, vol. 25(1), pages 36-65, January.
    2. Július Bemš & Caner Aydin, 2022. "Introduction to weather derivatives," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 11(3), May.
    3. Christensen, Troels Sønderby & Pircalabu, Anca & Høg, Esben, 2019. "A seasonal copula mixture for hedging the clean spark spread with wind power futures," Energy Economics, Elsevier, vol. 78(C), pages 64-80.
    4. Zong, Lu & Ender, Manuela, 2013. "Model Comparison for Temperature-based Weather Derivatives in Mainland China," Conference papers 332293, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    5. 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.
    6. Wolfgang Karl Härdle & Brenda López Cabrera & Awdesch Melzer, 2021. "Pricing wind power futures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1083-1102, August.
    7. Laura Casula & Guglielmo D’Amico & Giovanni Masala & Filippo Petroni, 2020. "Performance estimation of photovoltaic energy production," Letters in Spatial and Resource Sciences, Springer, vol. 13(3), pages 267-285, December.
    8. Lingohr, Daniel & Müller, Gernot, 2019. "Stochastic modeling of intraday photovoltaic power generation," Energy Economics, Elsevier, vol. 81(C), pages 175-186.

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