Deeply Learning Derivatives
This addresses computational speed issues in finance for derivative pricing, though it appears incremental as it applies existing deep learning methods to a known problem.
The paper tackles derivative valuation by applying deep learning to price call options on stock baskets, achieving valuations a million times faster than traditional models with high accuracy.
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 develop a methodology to randomly generate appropriate training data and explore the impact of several parameters including layer width and depth, training data quality and quantity on model speed and accuracy.