Implied Volatility Surface:The Model Framework and the Empirical Evidence
基于恒生指数期权数据，建立五因子随机隐含波动率曲面模型，并用扩展卡尔曼滤波估计参数，发现该方法优于传统两步法，且模型能更好捕捉隐含波动率曲面的动态行为。
This paper establishes a five-factor stochastic implied volatility 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.