EvoWorkshops 2008: Applications of Evolutionary Computing pp 113-122 | Cite as

Option Model Calibration Using a Bacterial Foraging Optimization Algorithm

  • Jing Dang
  • Anthony Brabazon
  • Michael O’Neill
  • David Edelman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4974)

Abstract

The Bacterial Foraging Optimization (BFO) algorithm is a biologically inspired computation technique which is based on mimicking the foraging behavior of E.coli bacteria. This paper illustrates how a BFO algorithm can be constructed and applied to solve parameter estimation of a EGARCH-M model which is then used for calibration of a volatility option pricing model. The results from the algorithm are shown to be robust and extendable, suggesting the potential of applying the BFO for financial modeling.

Keywords

Conditional Variance Optimal Power Flow Investor Sentiment Option Price Model Volatility Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jing Dang
    • 1
    • 2
  • Anthony Brabazon
    • 1
  • Michael O’Neill
    • 1
  • David Edelman
    • 2
  1. 1.Natural Computing Research and Applications GroupUniversity College DublinIreland
  2. 2.School of BusinessUniversity College DublinIreland

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