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Option Model Calibration Using a Bacterial Foraging Optimization Algorithm

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Applications of Evolutionary Computing (EvoWorkshops 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4974))

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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.

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References

  1. Brabazon, A., O’Neill, M.: Biologically-inspired Algorithms for Financial Modelling. Springer, Berlin (2006)

    MATH  Google Scholar 

  2. Bollerslev, T.: Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics 31(3), 307–327 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  3. Detemple, J.-B., Osakwe, C.: The Valuation of Volatility Options. European Finance Review 4(1), 21–50 (2000)

    Article  MATH  Google Scholar 

  4. Duan, J.-C.: Augmented GARCH (p,q) Process and its Diffusion Limit. Journal of Econometrics 79(1), 97–127 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  5. Engle, R.-F.: Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica 50(4), 987–1008 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  6. Hentschel, L.: All in the Family: Nesting Symmetric and Asymmetric GARCH Models. Journal of Financial Economics 39(1), 71–104 (1995)

    Article  Google Scholar 

  7. Kim, D.-H., Cho, J.-H.: Intelligent Control of AVR System Using GA-BF. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3684, pp. 854–859. Springer, Heidelberg (2005)

    Google Scholar 

  8. Kim, D.-H., Abraham, A., Cho, J.-H.: A Hybrid Genetic Algorithm and Bacterial Foraging Approach for Global Optimization. Information Sciences 177(18), 3918–3937 (2007)

    Article  Google Scholar 

  9. Li, M.-S., Tang, W.-J., et al.: Bacterial Foraging Algorithm with Varying Population for Optimal Power Flow. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 32–41. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  10. Mishra, S.: A Hybrid Least Square-Fuzzy Bacterial Foraging Strategy for Harmonic Estimation. IEEE Transactions on Evolutionary Computation 9(1), 61–73 (2005)

    Article  Google Scholar 

  11. Mishra, S., Bhende, C.-N.: Bacterial Foraging Technique-Based Optimized Active Power Filter for Load Compensation. IEEE Transactions on Power Delivery 22(1), 457–465 (2007)

    Article  Google Scholar 

  12. Nelson, D.-B.: ARCH Models as Diffusion Approximations. Journal of Econometrics 45(1–2), 7–38 (1990)

    Article  MathSciNet  Google Scholar 

  13. Nelson, D.-B.: Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica 59(2), 347–370 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  14. Passino, K.-M.: Biomimicry of bacterial foraging for distributed optimization and control. Control Systems Magazine, IEEE 22(3), 52–67 (2002)

    Article  MathSciNet  Google Scholar 

  15. St. Pierre, E.F.: Estimating EGARCH-M models: Science or art? The Quarterly Review of Economics and Finance 38(2), 167–180 (1998)

    Article  Google Scholar 

  16. Tang, W.-J., Wu, Q.-H., Saunders, J.-R.: A Novel Model for Bacterial Foraging in Varying Environments. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3980, pp. 556–565. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  17. Tang, W.-J., Wu, Q.-H., Saunders, J.-R.: Bacterial Foraging Algorithm for Dynamic Environments. IEEE Congress on Evolutionary Computation, 1324–1330 (July 2006)

    Google Scholar 

  18. Tang, W.-J., Wu, Q.-H., Saunders, J.-R.: Individual-Based Modeling of Bacterial Foraging with Quorum Sensing in a Time-Varying Environment. In: Marchiori, E., Moore, J.H., Rajapakse, J.C. (eds.) EvoBIO 2007. LNCS, vol. 4447, pp. 280–290. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  19. Ulagammai, M., Venkatesh, P., et al.: Application of Bacterial Foraging Technique Trained Artificial and Wavelet Neural Networks in Load Forecasting. Neurocomputing 70(16-18), 2659–2667 (2007)

    Article  Google Scholar 

  20. Original matlab codes of Passino can be obtained from, http://www.ece.osu.edu/~passino/ICbook/ic_index.html

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Author information

Authors and Affiliations

  1. Natural Computing Research and Applications Group, University College Dublin, Ireland

    Jing Dang, Anthony Brabazon & Michael O’Neill

  2. School of Business, University College Dublin, Ireland

    Jing Dang & David Edelman

Authors
  1. Jing Dang
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  2. Anthony Brabazon
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  3. Michael O’Neill
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  4. David Edelman
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Editor information

Mario Giacobini Anthony Brabazon Stefano Cagnoni Gianni A. Di Caro Rolf Drechsler Anikó Ekárt Anna Isabel Esparcia-Alcázar Muddassar Farooq Andreas Fink Jon McCormack Michael O’Neill Juan Romero Franz Rothlauf Giovanni Squillero A. Şima Uyar Shengxiang Yang

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© 2008 Springer-Verlag Berlin Heidelberg

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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

  • Print ISBN: 978-3-540-78760-0

  • Online ISBN: 978-3-540-78761-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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