Option Model Calibration Using a Bacterial Foraging Optimization Algorithm

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.

[1]  Daniel B. Nelson.  CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACH , 1991 .

[2]  Q. Henry Wu,et al.  A Novel Model for Bacterial Foraging in Varying Environments , 2006, ICCSA.

[3]  Eileen F. St. Pierre,et al.  Estimating EGARCH-M models: Science or art? , 1998 .

[4]  Daniel B. Nelson.  ARCH models as diffusion approximations , 1990 .

[5]  C. N. Bhende,et al.  Bacterial Foraging Technique-Based Optimized Active Power Filter for Load Compensation , 2007, IEEE Transactions on Power Delivery.

[6]  M. Ulagammai,et al.  Application of bacterial foraging technique trained artificial and wavelet neural networks in load forecasting , 2007, Neurocomputing.

[7]  Dong Hwa Kim,et al.  A hybrid genetic algorithm and bacterial foraging approach for global optimization , 2007, Inf. Sci..

[8]  Dong Hwa Kim,et al.  Intelligent Control of AVR System Using GA-BF , 2005, KES.

[9]  Lakhmi C. Jain,et al.  Knowledge-Based Intelligent Information and Engineering Systems , 2004, Lecture Notes in Computer Science.

[10]  J. Detemple,et al.  The Valuation of Volatility Options , 2000 .

[11]  Q. Henry Wu,et al.  Bacterial Foraging Algorithm For Dynamic Environments , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[12]  Michael O'Neill,et al.  Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series) , 2005 .

[13]  Q. Henry Wu,et al.  Individual-Based Modeling of Bacterial Foraging with Quorum Sensing in a Time-Varying Environment , 2007, EvoBIO.

[14]  Sukumar Mishra,et al.  A hybrid least square-fuzzy bacterial foraging strategy for harmonic estimation , 2005, IEEE Transactions on Evolutionary Computation.

[15]  Mak Kaboudan,et al.  Biologically Inspired Algorithms for Financial Modelling , 2006, Genetic Programming and Evolvable Machines.

[16]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[17]  David Taniar,et al.  Computational Science and Its Applications - ICCSA 2006, International Conference, Glasgow, UK, May 8-11, 2006, Proceedings, Part I , 2006, ICCSA.

[18]  William B. White,et al.  All in the Family , 2005 .

[19]  Q. Henry Wu,et al.  Bacterial Foraging Algorithm with Varying Population for Optimal Power Flow , 2009, EvoWorkshops.

[20]  Jin-Chuan Duan,et al.  Augmented GARCH (p,q) process and its diffusion limit , 1997 .

[21]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[22]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[23]  Rolf Drechsler,et al.  Applications of Evolutionary Computing, EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings , 2008, EvoWorkshops.