Deeply Learning Derivatives
14 Pages Posted: 9 Oct 2018 Last revised: 20 Oct 2018
Date Written: October 14, 2018
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: Deep Learning, Neural Networks, Monte Carlo, Basket Options, GPU, Quantitative Finance, XVA, Valuation
JEL Classification: C13, C15, C44, C51, C52, C63, D40, G12, G13, G15, G21, G28, K22, M40
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