  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.
