Option Pricing Model Based on Newton-Raphson Iteration and RBF Neural Network Using Implied Volatility

Yan LIN, Jianhui YANG

Abstract


As option is a kind of significant financial derivatives, option pricing will affect both the risk and profit of the investment. This paper proposed an option pricing model based on RBF neural network combined with the Newton-Raphson iteration method which is used to obtain the implied volatility.
First, considering implied volatility includes investors’ expectation about the changes of future price options. Newton-Raphson iteration method is used to obtain the implied volatility by rolling estimation which is also added into the RBF neural network model.
Then, RBF neural network is trained based on Black-Scholes model. Self-organizing learning and the least square method are used to optimize the parameters of RBF neural network.
At last, empirical study and analysis with 10 50ETF stock options chosen from Shanghai Stock Exchange market have been performed, the result shows that the accuracy of the proposed model is better than the traditional BP neural network and B-S model and the effect of option pricing using by implied volatility is also better than others.


Keywords


Option pricing; Newton-Raphson; RBF neural network; Implied volatility

Full Text:

PDF

References


Black, F., & Scholes, M. (1973). The pricing of options and corporate 1iabilities. Journal of Political Economy, 81(7), 637-655.

Gencay, R., & Qi, M. (2001). Pricing and hedging derivatives securities with neural networks: Bayesian regularization, early stopping and bagging. IEEE Transactions on Neural Networks, 12(4), 726-734.

Hutchinson et al. (1994). A non-parametric approach to pricing and hedging derivative securities via learning networks. Journal of Finance, 49(3), 885-889.

Laibcygier, P., Flitman, A., Swan, A., & Hyndman, R. (1997). The pricing and trading of options using a hybrid neural networks models with historical volatility. Neurovest Journal, 5, 27-41.

Merton, R., 1973, The theory of rational option pricing. Bell Journal of Economics and Management Sciences, 4(1), 141-183.

Zhang, H. Y., & Lin, H. (2009). Option price forecasting model by applying hybrid neural network and genetic algorithm. Journal of Industrial Engineering Management, 123(1), 59-87.

Zhang, W. F., & Cui, X. Y. (2007). The estimation of option implied volatility. Friends of Accounting, (3), 6-7.




DOI: http://dx.doi.org/10.3968/8730

Refbacks

  • There are currently no refbacks.


Copyright (c) 2016 Yan LIN, Jianhui YANG

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


Share us to:   


Reminder

  • How to do online submission to another Journal?
  • If you have already registered in Journal A, then how can you submit another article to Journal B? It takes two steps to make it happen:

1. Register yourself in Journal B as an Author

  • Find the journal you want to submit to in CATEGORIES, click on “VIEW JOURNAL”, “Online Submissions”, “GO TO LOGIN” and “Edit My Profile”. Check “Author” on the “Edit Profile” page, then “Save”.

2. Submission

  • Go to “User Home”, and click on “Author” under the name of Journal B. You may start a New Submission by clicking on “CLICK HERE”.
  • We only use three mailboxes as follows to deal with issues about paper acceptance, payment and submission of electronic versions of our journals to databases: caooc@hotmail.com; css@cscanada.net; css@cscanada.org

 Articles published in Canadian Social Science are licensed under Creative Commons Attribution 4.0 (CC-BY).

 CANADIAN SOCIAL SCIENCE Editorial Office 

Address1020 Bouvier Street, Suite 400, Quebec City, Quebec, G2K 0K9, Canada.

Website: Http://www.cscanada.net Http://www.cscanada.org 
E-mailcss@cscanada.net, css@cscanada.org

Copyright © Canadian Academy of Oriental and Occidental Culture