文章基本信息

标题：Option Pricing Model Based on Newton-Raphson Iteration and RBF Neural Network Using Implied Volatility
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作者：Yan LIN ; Jianhui YANG
期刊名称：Canadian Social Science
印刷版ISSN：1712-8056
电子版ISSN：1923-6697
出版年度：2016
卷号：12
期号：8
页码：25-29
DOI：10.3968/8730
语种：English
出版社：Canadian Academy of Oriental and Occidental Culture
摘要：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.
其他摘要：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.
关键词：Option pricing;Newton-Raphson;RBF neural network;Implied volatility