[1]张鸿彦,林辉.基于小波神经网络的期权定价模型[J].东南大学学报(自然科学版),2007,37(4):716-720.[doi:10.3969/j.issn.1001-0505.2007.04.034]
 Zhang Hongyan,Lin Hui.Option pricing models based on wavelet neural network[J].Journal of Southeast University (Natural Science Edition),2007,37(4):716-720.[doi:10.3969/j.issn.1001-0505.2007.04.034]
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基于小波神经网络的期权定价模型()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
37
期数:
2007年第4期
页码:
716-720
栏目:
经济与管理
出版日期:
2007-07-20

文章信息/Info

Title:
Option pricing models based on wavelet neural network
作者:
张鸿彦1 林辉2
1 东南大学系统工程研究所, 南京 210096; 2 南京大学商学院, 南京 210093
Author(s):
Zhang Hongyan1 Lin Hui2
1 Institute of System Engineering, Southeast University, Nanjing 210096, China
2 School of Business, Nanjing University, Nanjing 210093, China
关键词:
期权定价 小波神经网络 Black-Scholes 模型 隐含波动率
Keywords:
option pricing wavelet neural network Black-Scholes model implied volatility
分类号:
F830.9
DOI:
10.3969/j.issn.1001-0505.2007.04.034
摘要:
Black-Scholes模型所要求的假设条件在真实的市场条件下往往不能满足.提出了一种新的应用小波神经网络进行预测的欧式期权定价模型.将期权按钱性进行分类, 以一种新的加权的隐含波动率作为神经网络的输入变量,通过小波神经网络模型、BP网络模型和Black-Scholes模型来预测香港恒指买权的价格.实证结果表明,将一种加权的隐含波动率作为输入变量的小波神经网络模型优于Black-Scholes模型和其他神经网络模型.因此该模型可以更有效地预测欧式期权价格.
Abstract:
The option markets often violate most of the Black-Scholes model’s assumptions. A new European option price forecasting model by applying wavelet neural network is proposed. In such an approach, option partition according to moneyness is applied. Herein, the ability is compared among the wavelet neural network models, the back propagation(BP)neural network models and the Black-Scholes model to price HSI(Hang Seng index)call options. An implied volatility measure is used. The experimental results show that when a weighted implied volatility measure is regarded as an input, the performance of the wavelet neural network is better than that of other neural network models and the Black-Scholes model. Therefore, this model can be used to foresee European option price more effectively.

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备注/Memo

备注/Memo:
基金项目: 国家自然科学基金资助项目(70501013).
作者简介: 张鸿彦(1972—),男,博士生,zhanghongyan72@sina.com.
更新日期/Last Update: 2007-07-20