隐含波动率曲面:建模与实证

Implied Volatility Surface:The Model Framework and the Empirical Evidence

本文利用香港恒生指数期权的数据,对隐含波动率曲面动态过程进行建模和估计,建立起了一个五因子随机隐含波动率模型。在模型的估计方法上,本文首次引入了基于小样本面板数据的扩展的卡尔曼滤波法。结果显示,在香港市场上,扩展的卡尔曼滤波法比传统的两步法可以得到更好的估计结果,本文建立起来的五因子随机隐含波动率模型能很好地刻画恒指期权隐含波动率曲面的变动规律,效果明显优于静态隐含波动率模型。

This paper establishes a five-factor stochastic implied volatihty surface model based on the data of Hang Seng Index of options market, and then firstly uses the extended Kalman Filter method for incomplete panel data to estimate the model parameters. The results show that in Hong Kong market, the extended Kalman Filter method is better than the traditional two-step method, and the five-factor stochastic implied volatility model does a much better job in capturing the dynamic behavior of the implied volatility surface than the determined or static implied volatility model.