Abstract
Nonparametric approaches of option pricing have recently emerged as alternative approaches that complement traditional parametric approaches. In this paper, we propose a novel neural network learning algorithm for option-pricing, which is a nonparametric approach. The proposed method is devised to improve generalization and computing time. Experimental results are conducted for the KOSPI200 index daily call options and demonstrate a significant performance improvement to reduce test error compared to other existing techniques.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Barhen, J., Protopopescu, V., Reister, D.: TRUST: A Determionistic Algorithm for Global Optimization. Science 276, 1094–1097 (1997)
Bakshi, G., Cao, C., Chen, Z.: Empirical Performance of Alternative Option-Pricing Models. The Journal of Finance 52(5), 2003–2049 (1997)
Gencay, R., Qi, M.: Pricing and Hedging Derivative Securities with Neural Networks: Bayesian Regularization, Early Stopping, and Bagging. IEEE Transactions on Neural Networks 12(4), 726–734 (2001)
Hagan, M.T., Menhaj, M.: Training Feedforward Networks with the Marquart Algorithm. IEEE Transactions on Neural Networks 5(6), 989–993 (1994)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, New York (1999)
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)
Lajbcygier, P.: Literature Review: The Problem with Modern Parametric Option Pricing. Journal of Computational Intelligence (1999)
Nocedal, J., Wright, S.J.: Numerical Optimization. Springer, New York (1999)
RoyChowdhury, P., Singh, Y., Chansarkar, A.: Dynamic Tunneling Technique for Efficient Training of Multilayer Perceptrons. IEEE Transactions on Neural Networks 10(1), 48–55 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Choi, HJ., Lee, HS., Han, GS., Lee, J. (2004). Efficient Option Pricing via a Globally Regularized Neural Network. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_157
Download citation
DOI: https://doi.org/10.1007/978-3-540-28648-6_157
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
eBook Packages: Springer Book Archive