Deeply Learning Derivatives

Ryan Ferguson and Andrew Green

Papers from arXiv.org

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.

Date: 2018-09, Revised 2018-10
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1809.02233

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