Model-Agnostic Pricing of Exotic Derivatives Using Signatures

Andrew Alden, Carmine Ventre, Blanka Horvath, Gordon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Neural networks hold out the promise of fast and reliable derivative pricing. Such an approach usually involves the supervised learning task of mapping contract and model parameters to derivative prices. In this work, we introduce a model-agnostic path-wise approach to derivative pricing using higher-order distribution regression. Our methodology leverages the 2nd-order Maximum Mean Discrepancy (MMD), a notion of distance between stochastic processes based on path signatures. To overcome the high computational cost of its calculation, we pre-train a neural network that can quickly and accurately compute higher-order MMDs. This allows the combination of distribution regression with neural networks in a computationally feasible way. We test our model on down-and-in barrier options. We demonstrate that our path-wise approach extends well to the high-dimensional case by applying it to rainbow options and autocallables. Our approach has a significant speed-up over Monte Carlo pricing.

Original languageEnglish
Title of host publicationProceedings of the 3rd ACM International Conference on AI in Finance, ICAIF 2022
PublisherAssociation for Computing Machinery, Inc
Pages96-104
Number of pages9
ISBN (Electronic)9781450393768
DOIs
StatePublished - 2 Nov 2022
Externally publishedYes
Event3rd ACM International Conference on AI in Finance, ICAIF 2022 - New York, United States
Duration: 2 Nov 20224 Nov 2022

Publication series

NameProceedings of the 3rd ACM International Conference on AI in Finance, ICAIF 2022

Conference

Conference3rd ACM International Conference on AI in Finance, ICAIF 2022
Country/TerritoryUnited States
CityNew York
Period2/11/224/11/22

Keywords

  • Autocallables
  • Barrier Options
  • Fast Derivative Pricing
  • Path Signature
  • Rainbow Options

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