Deep Learning for Exotic Option Valuation

TL;DR

This paper introduces the volatility feature approach (VFA), a neural network-based method for exotic option valuation that preserves model structure and outperforms traditional calibration methods in accuracy and speed.

Contribution

The paper proposes VFA, a novel neural network approach that maintains model structure and improves exotic option valuation over the traditional model calibration approach.

Findings

VFA outperforms MCA in practical volatility surface scenarios.

VFA provides faster exotic option valuation after initial training.

VFA maintains consistency with the user's preferred model structure.

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. 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.