  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). This paper
considers an alternative approach where the points on the volatility surface
are features input to a neural network. We refer to this as the volatility
feature approach (VFA). 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. VFA is a useful tool
for the estimation of model risk. We illustrate this using S&P 500 data for the
2001 to 2019 period.
