A Novel Learning Network for Option Pricing with Confidence Interval Information

  • Kyu-Hwan Jung
  • Hyun-Chul Kim
  • Jaewook Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


Nonparametric approaches for option pricing have recently emerged as alternative approaches that complement traditional parametric approaches. In this paper, we propose a novel learning network for option-pricing, which is a nonparametric approach. The main advantages of the proposed method are providing a principled hyper-parameter selection method and the distribution of predicted target value. With these features, we do not need to adjust any parameters at hand for model learning and we can get confidence interval as well as strict predicted target value. Experiments are conducted for the KOSPI200 index daily call options and their results show that the proposed method works excellently to obtain prediction confidence interval and to improve the option-pricing accuracy.


Mean Square Error Stock Price Gaussian Process Option Price Implied Volatility 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bakshi, G., Cao, C., Chen, Z.: Empirical Performance of Alternative Option-Pricing Models. The Journal of Finance 52(5), 2003–2049 (1997)CrossRefGoogle Scholar
  2. 2.
    Choi, H.-J., Lee, D., Lee, J.: Efficient Option Pricing via a Globally Regularized Neural Network. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 988–993. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Gencay, R., Qi, M.: Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and begging. IEEE Transactions on Neural Networks 12(4), 726–734 (2001)CrossRefGoogle Scholar
  4. 4.
    Gibbs, M., Mackay, D.J.C.: Efficient Implementation of Gaussian Processes. Draft Manuscript (1992), http://citeseer.nj.nec.com/6641.html
  5. 5.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, New York (1999)MATHGoogle Scholar
  6. 6.
    Hutchinson, J.M., Lo, A.W., Poggio, T.: A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks. Journal of Finance 49, 851–889 (1994)CrossRefGoogle Scholar
  7. 7.
    Lajbcygier, P.: Literature Review: The Problem with Modern Parametric Option Pricing. Journal of Computational Intelligence in Finance 7(5), 6–23 (1999)Google Scholar
  8. 8.
    Lee, J., Lee, D.: An Improved Cluster Labeling Method for Support Vector Clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(3), 461–464 (2005)CrossRefGoogle Scholar
  9. 9.
    Neal, R.M.: Bayesian Learning for Neural Networks. Lecture Notes in Statistics. Springer, New York (1996)MATHGoogle Scholar
  10. 10.
    Neal, R.M.: Regression and Classification Using Gaussian Process Priors. Bayesian Statistics 6, 465–501 (1998)Google Scholar
  11. 11.
    Rasmussen, C.E.: Evaluation of Gaussian Processes and Other Methods for Non-Linear Regression. PhD Thesis University of Toronto (1996)Google Scholar
  12. 12.
    Williams, C.K.I., Rasmussen, C.E.: Gaussian Processes for Regression. NIPS 8, 514–520 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kyu-Hwan Jung
    • 1
  • Hyun-Chul Kim
    • 1
  • Jaewook Lee
    • 1
  1. 1.Department of Industrial and Management EngineeringPohang University of Science and TechnologyPohang, KyungbukKorea

Personalised recommendations