A common approach to valuing exotic options involves choosing a model and then determining its parameters to fit the volatility surface as closely as possible. We refer to this as the model calibration approach (MCA). A disadvantage of MCA is that some information in the volatility surface is lost during the calibration process and the prices of exotic options will not in general be consistent with those of plain vanilla options. We consider an alternative approach where the structure of the user's preferred model is preserved but points on the volatility are features input to a neural network. We refer to this as the volatility feature approach (VFA) model. We conduct experiments showing that VFA can be expected to outperform MCA for the volatility surfaces encountered in practice. Once the upfront computational time has been invested in developing the neural network, the valuation of exotic options using VFA is very fast.

翻译：评估外来选择的共同方法涉及选择一个模型,然后确定尽可能贴近挥发性表面的参数,我们将此称为模型校准方法(MCA)。MCA的缺点是,在校准过程中,挥发性表面的某些信息丢失,外来选择的价格一般与普通香草选择的价格不一致。我们考虑另一种方法,即保留用户首选模式的结构,但关于挥发性的要点是神经网络的特征输入。我们将此称为挥发性特征方法(VFA)模型。我们进行实验,表明预期挥发性表面在实际遇到的挥发性表面会超过MCA。一旦前期计算时间用于开发神经网络,使用VFA的外来选择的估值非常快。