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Gas storage valuation applying numerically constructed recombining trees

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  • Felix, Bastian Joachim
  • Weber, Christoph

Abstract

The liberalization of European natural gas markets forces market participants to base their decisions on market prices. For owners and operators of natural gas storage facilities it is therefore necessary to take market prices into account for their decisions. In this framework this paper provides a new approach for the valuation of natural gas storage facilities. Using stochastic dynamic programming on multinomial recombining trees, the optimal storage strategy and value are determined. For this we (i) estimate the deterministic and random impacts on natural gas prices, (ii) simulate gas prices considering the results of the first step, (iii) construct numerically the recombining tree using the simulation results, (iv) determine the optimal storage strategy and value. Besides the determination of the optimal storage value and operation schedule the value quantiles are calculated. Via the quantiles relevant risk measures like value at risk and conditional value at risk are determined.

Suggested Citation

  • Felix, Bastian Joachim & Weber, Christoph, 2012. "Gas storage valuation applying numerically constructed recombining trees," European Journal of Operational Research, Elsevier, vol. 216(1), pages 178-187.
    • Handle: RePEc:eee:ejores:v:216:y:2012:i:1:p:178-187
    DOI: 10.1016/j.ejor.2011.07.029
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    2. Anna Maria Gambaro & Nicola Secomandi, 2021. "A Discussion of Non‐Gaussian Price Processes for Energy and Commodity Operations," Production and Operations Management, Production and Operations Management Society, vol. 30(1), pages 47-67, January.
    3. Cummins, Mark & Kiely, Greg & Murphy, Bernard, 2018. "Gas storage valuation under multifactor Lévy processes," Journal of Banking & Finance, Elsevier, vol. 95(C), pages 167-184.
    4. Keles, Dogan & Dehler-Holland, Joris, 2022. "Evaluation of photovoltaic storage systems on energy markets under uncertainty using stochastic dynamic programming," Energy Economics, Elsevier, vol. 106(C).
    5. Michael Ludkovski & Aditya Maheshwari, 2018. "Simulation Methods for Stochastic Storage Problems: A Statistical Learning Perspective," Papers 1803.11309, arXiv.org.
    6. Selvaprabu Nadarajah & François Margot & Nicola Secomandi, 2015. "Relaxations of Approximate Linear Programs for the Real Option Management of Commodity Storage," Management Science, INFORMS, vol. 61(12), pages 3054-3076, December.
    7. Hanfeld, Marc & Schlüter, Stephan, 2016. "Operating a swing option on today's gas markets: How least squares Monte Carlo works and why it is beneficial," FAU Discussion Papers in Economics 10/2016, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    8. Lin, Jing & Mou, Dunguo, 2021. "Analysis of the optimal spatial distribution of natural gas under ‘transition from coal to gas’ in China," Resource and Energy Economics, Elsevier, vol. 66(C).
    9. Bastian Felix, 2012. "Gas Storage Valuation: A Comparative Simulation Study," EWL Working Papers 1201, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Apr 2014.
    10. Löhndorf, Nils & Wozabal, David, 2021. "Gas storage valuation in incomplete markets," European Journal of Operational Research, Elsevier, vol. 288(1), pages 318-330.
    11. Trigeorgis, Lenos & Tsekrekos, Andrianos E., 2018. "Real Options in Operations Research: A Review," European Journal of Operational Research, Elsevier, vol. 270(1), pages 1-24.
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