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Financial markets for weather

In: Modeling and Pricing in Financial Markets for Weather Derivatives

Author

Listed:
  • Fred Espen Benth

    (University of Oslo, Norway)

  • Jūratė Šaltytė Benth

    (University of Oslo, Norway)

Abstract

By the year 2011, the notional value of the market for weather derivatives was USD11.8 billion, according to the Weather Risk Management Association (WRMA). The weather markets have grown remarkably in financial strength since the first known weather deals took place in 1996. Nowadays, these markets provide a platform for managing risk exposure in weather variables like temperature, wind and precipitation. The most liquid weather derivatives are based on temperature. In short, these derivatives convert weather into money, where you can profit on bad weather (or good, for that matter).In this monograph we analyze typical weather derivatives traded in the market, both over-the-counter and as customary assets on exchanges. Our aim is to present a unified approach to the statistical modelling of weather factors like temperature, wind speed and precipitation, and apply these models along with the arbitrage theory of mathematical finance to price weather derivatives. In this first Chapter we present typical weather derivatives and the markets they are traded in, before we move on with the statistical analysis of weather and the pricing of weather derivatives.

Suggested Citation

  • Fred Espen Benth & Jūratė Šaltytė Benth, 2012. "Financial markets for weather," World Scientific Book Chapters, in: Modeling and Pricing in Financial Markets for Weather Derivatives, chapter 1, pages 1-13, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789814401852_0001
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    Citations

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    Cited by:

    1. Július Bemš & Caner Aydin, 2022. "Introduction to weather derivatives," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 11(3), May.
    2. Deschatre, Thomas & Féron, Olivier & Gruet, Pierre, 2021. "A survey of electricity spot and futures price models for risk management applications," Energy Economics, Elsevier, vol. 102(C).
    3. Johannes Kaufmann & Philipp Artur Kienscherf & Wolfgang Ketter, 2020. "Modeling and Managing Joint Price and Volumetric Risk for Volatile Electricity Portfolios," Energies, MDPI, vol. 13(14), pages 1-19, July.
    4. Kononovicius, Aleksejus & Kazakevičius, Rytis & Kaulakys, Bronislovas, 2022. "Resemblance of the power-law scaling behavior of a non-Markovian and nonlinear point processes," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    5. Simona Franzoni & Cristian Pelizzari, 2021. "Rainfall option impact on profits of the hospitality industry through scenario correlation and copulas," Annals of Operations Research, Springer, vol. 299(1), pages 939-962, April.
    6. Fred Espen Benth & Heidar Eyjolfsson, 2015. "Representation and approximation of ambit fields in Hilbert space," Papers 1509.08272, arXiv.org.
    7. 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.
    8. 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.
    9. Fred Espen Benth & Sara Ana Solanilla Blanco, 2015. "Forward Prices As Functionals Of The Spot Path In Commodity Markets Modeled By Levy Semistationary Processes," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 1-35.
    10. Pircalabu, A. & Hvolby, T. & Jung, J. & Høg, E., 2017. "Joint price and volumetric risk in wind power trading: A copula approach," Energy Economics, Elsevier, vol. 62(C), pages 139-154.
    11. Fred Espen Benth & Luca Di Persio & Silvia Lavagnini, 2018. "Stochastic Modeling of Wind Derivatives in Energy Markets," Risks, MDPI, vol. 6(2), pages 1-21, May.
    12. Aleksejus Kononovicius & Rytis Kazakeviv{c}ius & Bronislovas Kaulakys, 2022. "Resemblance of the power-law scaling behavior of a non-Markovian and nonlinear point processes," Papers 2205.07563, arXiv.org, revised Jul 2022.
    13. Yeny E. Rodríguez & Miguel A. Pérez-Uribe & Javier Contreras, 2021. "Wind Put Barrier Options Pricing Based on the Nordix Index," Energies, MDPI, vol. 14(4), pages 1-14, February.
    14. Fred Espen Benth & Marco Piccirilli & Tiziano Vargiolu, 2017. "Additive energy forward curves in a Heath-Jarrow-Morton framework," Papers 1709.03310, arXiv.org, revised Jun 2018.
    15. Sonja Cox & Sven Karbach & Asma Khedher, 2022. "An infinite‐dimensional affine stochastic volatility model," Mathematical Finance, Wiley Blackwell, vol. 32(3), pages 878-906, July.
    16. Fred Espen Benth & Asma Khedher & Mich`ele Vanmaele, 2017. "Pricing of commodity derivatives on processes with memory," Papers 1711.00307, arXiv.org.
    17. Fei Gao & Shuaiqiang Liu & Cornelis W. Oosterlee & Nico M. Temme, 2022. "Solution of integrals with fractional Brownian motion for different Hurst indices," Papers 2203.02323, arXiv.org, revised Mar 2022.
    18. Markus Hess, 2021. "A new approach to wind power futures pricing," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1235-1252, December.
    19. Fred Espen Benth & Asma Khedher & Michèle Vanmaele, 2020. "Pricing of Commodity Derivatives on Processes with Memory," Risks, MDPI, vol. 8(1), pages 1-32, January.

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