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How to speed up the quantization tree algorithm with an application to swing options

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  • Anne Laure Bronstein
  • Gilles Pages
  • Benedikt Wilbertz

Abstract

In this paper, we suggest several improvements to the numerical implementation of the quantization method for stochastic control problems in order to obtain fast and accurate premium estimations. This technique is applied to derivative pricing in energy markets. Several ways of modeling energy derivatives are described and numerical examples including parallel execution on multi-processor devices are presented to illustrate the accuracy of these methods and their execution times.

Suggested Citation

  • Anne Laure Bronstein & Gilles Pages & Benedikt Wilbertz, 2010. "How to speed up the quantization tree algorithm with an application to swing options," Quantitative Finance, Taylor & Francis Journals, vol. 10(9), pages 995-1007.
    • Handle: RePEc:taf:quantf:v:10:y:2010:i:9:p:995-1007
    DOI: 10.1080/14697680903508487
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    References listed on IDEAS

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    1. Olivier Bardou & Sandrine Bouthemy & Gilles Pages, 2009. "Optimal Quantization for the Pricing of Swing Options," Applied Mathematical Finance, Taylor & Francis Journals, vol. 16(2), pages 183-217.
    2. Pagès Gilles & Printems Jacques, 2003. "Optimal quadratic quantization for numerics: the Gaussian case," Monte Carlo Methods and Applications, De Gruyter, vol. 9(2), pages 135-165, April.
    3. Ole E. Barndorff-Nielsen, 1997. "Processes of normal inverse Gaussian type," Finance and Stochastics, Springer, vol. 2(1), pages 41-68.
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    Cited by:

    1. S. Corsaro & D. Marazzina & Z. Marino, 2015. "A parallel wavelet-based pricing procedure for Asian options," Quantitative Finance, Taylor & Francis Journals, vol. 15(1), pages 101-113, January.
    2. Giorgia Callegaro & Luciano Campi & Valeria Giusto & Tiziano Vargiolu, 2017. "Utility indifference pricing and hedging for structured contracts in energy markets," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 85(2), pages 265-303, April.
    3. Gilles Pag`es & Benedikt Wilbertz, 2011. "GPGPUs in computational finance: Massive parallel computing for American style options," Papers 1101.3228, arXiv.org.
    4. Pagès, Gilles & Sagna, Abass, 2018. "Improved error bounds for quantization based numerical schemes for BSDE and nonlinear filtering," Stochastic Processes and their Applications, Elsevier, vol. 128(3), pages 847-883.

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