福州大学学报（自然科学版）2018，Vol.46Issue(2)：156-162,7.DOI:10.7631/issn.1000-2243.17084
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
摘要
Abstract
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.关键词
期权/非参数核回归/隐含波动率/Nadaraya-Watson核估计/Parzen-窗法Key words
options/nonparametric kernel regression/implied volatility/Nadaraya-Watson nuclear estimates/Parzen window分类
管理科学引用本文复制引用
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):156-162,7.基金项目
福建省自然科学基金资助项目(2015J01013) （2015J01013）
