Deeply Learning Derivatives
Computational Finance
2018-10-19 v4 Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.1809.02233,
title = {Deeply Learning Derivatives},
author = {Ryan Ferguson and Andrew Green},
journal= {arXiv preprint arXiv:1809.02233},
year = {2018}
}