Option Model Calibration Using a Bacterial Foraging Optimization Algorithm

Abstract

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.

A survey on cloud-based solutions for cloud manufacturing

“…The concept of bacterial forging algorithm (BFO) was first stated by Kevin M. Passino in order to deal with the concept of optimisation in distributed environment (Passino, 2012). This is based on the foraging behaviour of bacteria named Escherichia coli (E-coli) which lives in human intestine (Dang et al, 2008). The mode of selection reduces and recovers the strategies of foraging.…”

A Hybrid Bacterial Foraging Algorithm For Solving Job Shop Scheduling Problems

“…BFO algorithm was implemented various real world problems. Kim suggested that the BFO could be applied to find solutions for difficult engineering design problems [15].…”

“…HBFO methodology is implemented with no swarming effect (ie) j cc =0 [15]. Here time is considered as cost.…”

Natural Computing in Finance – A Review

“…Hence, global search heuristics such as the genetic algorithm can have utility in uncovering a high-quality set of parameters. Examples of the use of NC algorithms for model calibration include [30,45].…”