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. © 2008 Springer-Verlag Berlin Heidelberg.
Cite
CITATION STYLE
Dang, J., Brabazon, A., O’Neill, M., & Edelman, D. (2008). Option model calibration using a bacterial foraging optimization algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4974 LNCS, pp. 113–122). https://doi.org/10.1007/978-3-540-78761-7_12
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