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.
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Dang, J., Brabazon, A., O’Neill, M., Edelman, D. (2008). Option Model Calibration Using a Bacterial Foraging Optimization Algorithm. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2008. Lecture Notes in Computer Science, vol 4974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78761-7_12
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DOI: https://doi.org/10.1007/978-3-540-78761-7_12
Publisher Name: Springer, Berlin, Heidelberg
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