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A numerical algorithm for pricing electricity derivatives for jump-diffusion processes based on continuous time lattices

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  • Albanese, Claudio
  • Lo, Harry
  • Tompaidis, Stathis

Abstract

We present a numerical algorithm for pricing derivatives on electricity prices. The algorithm is based on approximating the generator of the underlying price process on a lattice of prices, resulting in an approximation of the stochastic process by a continuous time Markov chain. We numerically study the rate of convergence of the algorithm for the case of the Merton jump-diffusion model and apply the algorithm to calculate prices and sensitivities of both European and Bermudan electricity derivatives when the underlying price follows a stochastic process which exhibits both fast mean-reversion and jumps of large magnitude.

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  • Albanese, Claudio & Lo, Harry & Tompaidis, Stathis, 2012. "A numerical algorithm for pricing electricity derivatives for jump-diffusion processes based on continuous time lattices," European Journal of Operational Research, Elsevier, vol. 222(2), pages 361-368.
    • Handle: RePEc:eee:ejores:v:222:y:2012:i:2:p:361-368
    DOI: 10.1016/j.ejor.2012.04.030
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    References listed on IDEAS

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    1. Helyette Geman & A. Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," Post-Print halshs-00144198, HAL.
    2. Ross Baldick & Sergey Kolos & Stathis Tompaidis, 2006. "Interruptible Electricity Contracts from an Electricity Retailer's Point of View: Valuation and Optimal Interruption," Operations Research, INFORMS, vol. 54(4), pages 627-642, August.
    3. M. T. Barlow, 2002. "A Diffusion Model For Electricity Prices," Mathematical Finance, Wiley Blackwell, vol. 12(4), pages 287-298, October.
    4. repec:dau:papers:123456789/1433 is not listed on IDEAS
    5. Hélyette Geman & Andrea Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1225-1262, May.
    6. Albanese, Claudio, 2006. "Operator Methods, Abelian Processes And Dynamic Conditioning," MPRA Paper 5246, University Library of Munich, Germany, revised 06 Nov 2007.
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    Cited by:

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    4. Ethem Çanakoğlu & Esra Adıyeke, 2020. "Comparison of Electricity Spot Price Modelling and Risk Management Applications," Energies, MDPI, vol. 13(18), pages 1-22, September.
    5. Alexandre Lucas & Konstantinos Pegios & Evangelos Kotsakis & Dan Clarke, 2020. "Price Forecasting for the Balancing Energy Market Using Machine-Learning Regression," Energies, MDPI, vol. 13(20), pages 1-16, October.
    6. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    7. Kolb, Sebastian & Dillig, Marius & Plankenbühler, Thomas & Karl, Jürgen, 2020. "The impact of renewables on electricity prices in Germany - An update for the years 2014–2018," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    8. Mitra, Sovan & Date, Paresh & Mamon, Rogemar & Wang, I-Chieh, 2013. "Pricing and risk management of interest rate swaps," European Journal of Operational Research, Elsevier, vol. 228(1), pages 102-111.
    9. Islyaev, Suren & Date, Paresh, 2015. "Electricity futures price models: Calibration and forecasting," European Journal of Operational Research, Elsevier, vol. 247(1), pages 144-154.

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