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Risk coverage in the face of hydrological variability in a run-off hydraulic power plant using weather derivatives

Author

Listed:
  • José Hernández

    (Celsia S.A. ESP)

  • Fernando Carvajal-Serna

    (Universidad Nacional de Colombia, Medellín)

Abstract

Colombia’s power plant system has a 68 % share of hydraulic component and a 32 % share of thermic component. Such a composition makes the system vulnerable to hydrological variability. With the aim to develop a strategy to cover against hydrological risk in a run-off hydraulic power plant, this paper shows a methodology to build an index based on the theory of weather derivatives. The index, named charge for generation, is built using stream flows and precipitation as underlying variables. The results show that the index is an alternative to reduce the volatility of revenues from energy sales in 92 % in the case of stream flows, and in 38 % for precipitation. Thus, the proposed index is effective as coverage against hydrological risk

Suggested Citation

  • José Hernández & Fernando Carvajal-Serna, 2017. "Risk coverage in the face of hydrological variability in a run-off hydraulic power plant using weather derivatives," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 87, pages 191-222, Julio - D.
    • Handle: RePEc:lde:journl:y:2017:i:87:p:191-222
    DOI: 10.17533/udea.le.n87a07
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    References listed on IDEAS

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    1. Dorfleitner, Gregor & Wimmer, Maximilian, 2010. "The pricing of temperature futures at the Chicago Mercantile Exchange," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1360-1370, June.
    2. Kanamura, Takashi & Ohashi, Kazuhiko, 2009. "Pricing summer day options by good-deal bounds," Energy Economics, Elsevier, vol. 31(2), pages 289-297, March.
    3. Antonis Alexandridis K. & Achilleas D. Zapranis, 2013. "Weather Derivatives," Springer Books, Springer, edition 127, number 978-1-4614-6071-8, October.
    4. Mirela MATEI & Catalin VOICA, 2011. "The Temperature-Based Derivatives Contracts – New Products Of Weather Risk Industry," The Annals of the "Stefan cel Mare" University of Suceava. Fascicle of The Faculty of Economics and Public Administration, "Stefan cel Mare" University of Suceava, Romania, Faculty of Economics and Public Administration, vol. 11(1(13)), pages 141-147, June.
    5. Taylor, James W. & Buizza, Roberto, 2006. "Density forecasting for weather derivative pricing," International Journal of Forecasting, Elsevier, vol. 22(1), pages 29-42.
    6. Sandra Patricia Bello-Rodríguez & Robert Baudilio Beltrán-Ahumada, 2010. "Caracterización y pronóstico del precio spot de la energía eléctrica en Colombia," Revista de la Maestría de Derecho Económico, Universidad Javeriana - Derecho Económico, December.
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    More about this item

    Keywords

    Colombian power system; climate derivatives; run-off hydraulic power plant; risk coverage; hydrological risk.;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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