Deep Learning for Exotic Option Valuation
This addresses the issue of inconsistent pricing in financial derivatives for practitioners, though it is incremental as it builds on existing neural network applications in finance.
The paper tackles the problem of valuing exotic options by proposing a volatility feature approach (VFA) that uses neural networks to preserve volatility surface information, showing it outperforms traditional model calibration methods in practice and offers fast valuation after initial development.
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.