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Natural Computing in Computational Finance pp 109–127Cite as

Estimation of an EGARCH Volatility Option Pricing Model using a Bacteria Foraging Optimisation Algorithm

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 100))

Summary

The bacterial foraging optimisation algorithm is a novel natural computing algorithm which is based on mimicking the foraging behavior of E.coli bacteria. This chapter illustrates how a bacteria foraging optimisation algorithm (BFOA) can be constructed. The utility of this algorithm is tested by comparing its performance on a series of benchmark functions against that of the canonical genetic algorithm (GA). Following this, the algorithm’s performance is further assessed by applying it to estimate parameters for an EGARCH model which can then be applied for pricing volatility options. The results suggest that the BFOA can be used as a complementary technique to conventional statistical computing techniques in parameter estimation for financial models.

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Author information

Authors and Affiliations

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

    Jing Dang, Anthony Brabazon & Michael O’Neill

  2. School of Business, University College Dublin, Ireland

    Jing Dang & David Edelman

Authors
  1. Jing Dang
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  2. Anthony Brabazon
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  3. Michael O’Neill
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  4. David Edelman
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Editor information

Editors and Affiliations

  1. School of Business Quinn School, University College Dublin, Belfield, Dublin 4, Ireland

    Anthony Brabazon (Head of Research) (Head of Research)

  2. Natural Computing Research and Applications School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland

    Michael O’Neill (Director) (Director)

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© 2008 Springer-Verlag Berlin Heidelberg

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Dang, J., Brabazon, A., O’Neill, M., Edelman, D. (2008). Estimation of an EGARCH Volatility Option Pricing Model using a Bacteria Foraging Optimisation Algorithm. In: Brabazon, A., O’Neill, M. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77477-8_7

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  • DOI: https://doi.org/10.1007/978-3-540-77477-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77476-1

  • Online ISBN: 978-3-540-77477-8

  • eBook Packages: EngineeringEngineering (R0)

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