Applying Deep Learning to Derivatives Valuation

This paper uses deep learning to value derivatives. The approach is broadly applicable, and we use a call option on a basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models. We also develop a methodology to randomly generate appropriate training data and explore the impact of layer width and depth on neural network performance.