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Quantum Inspired Intelligent Systems pp 133–153Cite as

Calibration of the VGSSD Option Pricing Model using a Quantum-inspired Evolutionary Algorithm

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 121))

Quantum effects are a natural phenomenon and just like evolution, the brain, or immune systems, can serve as an inspiration for the design of computing algorithms. This chapter illustrates how a quantum-inspired evolutionary algorithm (QIEA) using real number encodings can be constructed and examines the utility of the resulting algorithm on an important real-world problem, namely the calibration of an Option Pricing model. The results from the algorithm are shown to be robust and comparable to those of other algorithms, suggesting that there is useful potential to apply QIEA to this domain.

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Author information

Authors and Affiliations

  1. Natural Computing Research and Applications Group, University College Dublin, Ireland

    Kai Fan, Anthony Brabazon & Michael O’Neill

  2. School of Business, University College Dublin, Ireland

    Kai Fan, Conall O’Sullivan & Seán McGarraghy

Authors
  1. Kai Fan
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  2. Conall O’Sullivan
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  3. Anthony Brabazon
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  4. Michael O’Neill
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  5. Seán McGarraghy
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Editor information

Editors and Affiliations

  1. Faculdade de Engenharia sala 5022-D, Universidade do Estado do Rio de Janeiro, Rua São Francisco Xavier 524, 20550-900, MARACANÃ-RJ, Brazil

    Nadia Nedjah  & Luiza de Macedo Mourelle  & 

  2. Centro de Ciências Exatas e de Tecnologia, Pontifícia Universidade Católica do Paraná, Rua Imaculada Conceição 1155, Curitiba-PR, 80215-901, Brazil

    Leandro dos Santos Coelho

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© 2008 Springer-Verlag Berlin Heidelberg

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Fan, K., O’Sullivan, C., Brabazon, A., O’Neill, M., McGarraghy, S. (2008). Calibration of the VGSSD Option Pricing Model using a Quantum-inspired Evolutionary Algorithm. In: Nedjah, N., Coelho, L.d.S., Mourelle, L.d.M. (eds) Quantum Inspired Intelligent Systems. Studies in Computational Intelligence, vol 121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78532-3_7

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  • DOI: https://doi.org/10.1007/978-3-540-78532-3_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78531-6

  • Online ISBN: 978-3-540-78532-3

  • eBook Packages: EngineeringEngineering (R0)

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