Deep Learning for Exotic Option Valuation

Abstract

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. This is referred to 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. The authors consider an alternative approach in which the structure of the user’s preferred model is preserved, but points on the volatility surface are features input to a neural network. They refer to this as the volatility feature approach (VFA) model. The authors 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.