Neural networks for option pricing and hedging: a literature review

2019/11/13 by Johannes Ruf, Ruf, Johannes, Weiguan Wang +1 · 16 citations
Computer Science · Economics, Econometrics and Finance · Mathematics · #91G20 #91G60 #91G70 #91G80 #Computational Finance (q-fin.CP) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Risk Management (q-fin.RM) #Statistical Finance (q-fin.ST) #cs.LG #msc:91G20 #msc:91G60 #msc:91G70 #msc:91G80 #q-fin.CP #q-fin.RM #q-fin.ST #stat.ML

paper · pdf · doi:10.48550/arxiv.1911.05620

Minor changes. Accepted for publications in Journal of Computational Finance

arxiv created 2020/05/09 · arxiv updated 2020/05/12

Abstract

Neural networks have been used as a nonparametric method for option pricing and hedging since the early 1990s. Far over a hundred papers have been published on this topic. This note intends to provide a comprehensive review. Papers are compared in terms of input features, output variables, benchmark models, performance measures, data partition methods, and underlying assets. Furthermore, related work and regularisation techniques are discussed.

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