Option model calibration using a bacterial foraging optimization algorithm

Authors:
Jing Dang;Anthony Brabazon;Michael O'Neill;David Edelman
Affiliations:
Natural Computing Research and Applications Group, University College Dublin, Ireland and School of Business, University College Dublin, Ireland;Natural Computing Research and Applications Group, University College Dublin, Ireland;Natural Computing Research and Applications Group, University College Dublin, Ireland;School of Business, University College Dublin, Ireland
Venue:
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Year:
2008

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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.