文章摘要
引用本文:戴秀菊,舒志彪.基于非参数核回归模型的隐含波动率预测[J].福州大学学报(自然科学版),2018,46(2):156~162
基于非参数核回归模型的隐含波动率预测
Implied volatility forecast based on nonparametric regression model
  
DOI:10.7631/issn.1000-2243.2018.02.156
中文关键词: 期权  非参数核回归  隐含波动率  Nadaraya-Watson核估计  Parzen-窗法
英文关键词: options  nonparametric kernel regression  implied volatility  Nadaraya-Watson nuclear estimates  Parzen window
基金项目:
作者单位
戴秀菊 福州大学数学与计算机科学学院福建 福州 350116 
舒志彪 福州大学数学与计算机科学学院福建 福州 350116 
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中文摘要:
      采用非参数核回归的方法,以市场上的期权数据为分析对象,将隐含波动率看作是与执行价格、剩余期限相关的函数,对其进行建模. 构建双窗宽Nadaraya-Watson高斯核回归模型和Parzen-窗均匀核回归模型,与已有的参数模型和Bourke模型进行实验对比. 实验结果表明,Parzen-窗均匀核回归模型的隐含波动率预测精度更高、效果更好,大样本的情况下优点更显著.
英文摘要:
      It is generally believed that the implied volatility is significantly correlated with strike price and time-to-maturity. This paper is mainly based on non-parametric kernel regression model to illustrate the implied volatility of stock option in terms of building two new models of the implied volatility,the double window Nadaraya-Watson Gaussian kernel regression model and Parzen window uniform kernel regression model. After experimentally compared these two models with the parametric model and the Bourke model,the result shows that the Parzen window uniform kernel regression model has better forecasting ability,especially when dealing with a large number of datasets.
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