Implied Stochastic Volatility Models
提出一种隐含随机波动率模型，通过将隐含波动率曲面的形状特征与模型系数函数直接关联，实现对非参数或参数随机波动率模型的稳定估计，基于标普500指数期权数据的实证表明该方法样本外表现良好。
Abstract This paper proposes “implied stochastic volatility models” designed to fit option-implied volatility data and implements a new estimation method for such models. The method is based on explicitly linking observed shape characteristics of the implied volatility surface to the coefficient functions that define the stochastic volatility model. The method can be applied to estimate a fully flexible nonparametric model, or to estimate by the generalized method of moments any arbitrary parametric stochastic volatility model, affine or not. Empirical evidence based on S&P 500 index options data show that the method is stable and performs well out of sample.