隐含波动率曲面的建模与预测

发布时间：2018-07-02 15:46

本文选题：隐含波动率曲面 + 半参数模型　；参考：《当代财经》2017年03期

【摘要】：根据隐含波动率期限结构与隐含波动率微笑特性提出的新的隐含波动率半参数模型,为隐含波动率曲面的建模提供了新的思路与方法。该模型包含九个具有现实经济含义的参数,分别对应剩余期限与在值程度两个因素的水平因子、斜率因子、曲度因子及其交互项因子。采用香港小型恒生指数期权数据,验证了在调整参数等于0.6时的模型能最优地拟合隐含波动率曲面。再根据样本期内日截面数据,估计出9个参数的时间序列,发现参数时间序列具有以交割日为峰值的周期性特征。利用MATLAB编程,分别实现了滚动加权平均法与BP神经网络法对参数的周期性时间序列进行外推预测,发现BP神经网络法明显优于滚动加权平均法。
[Abstract]:According to the term structure of implied volatility and smile characteristics of implied volatility, a new semi-parametric model of implicit volatility is proposed, which provides a new way of thinking and method for modeling implicit volatility surface. The model consists of nine parameters with realistic economic meaning, which correspond to the horizontal factor, slope factor, curvature factor and interaction factor of two factors, the residual period and the degree of value, respectively. Using the Hong Kong small Hang Seng Index option data, it is verified that the model can best fit the implied volatility surface when the adjustment parameter is equal to 0.6. Based on the daily cross section data of the sample period, the time series of 9 parameters are estimated, and it is found that the time series of the parameters have the periodicity characteristic of the peak value of the date of delivery. The rolling weighted average method and BP neural network method are used to extrapolate the periodic time series of parameters respectively by MATLAB programming. It is found that the BP neural network method is obviously superior to the rolling weighted average method.
【作者单位】：厦门大学管理学院;厦门大学经济学院金融系;
【基金】：国家自然科学基金面上项目“资产价格中隐含通货膨胀信息的提取、分析与应用”(71371161);国家自然科学基金面上项目“波动率微笑:隐含信息与动态建模”(71471155)
【分类号】：F224

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