Deeply Learning Derivatives
Ryan Ferguson and
Papers from arXiv.org
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.
New Economics Papers: this item is included in nep-big and nep-cmp
Date: 2018-09, Revised 2018-10
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1809.02233
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