Pricing options with a new hybrid neural network model

Yossi Shvimer, Song Ping Zhu

Research output: Contribution to journalArticlepeer-review

Abstract

A novel hybrid option pricing model using a deep learning neural network has been developed. The hybrid model keeps the traditional option pricing model with the same input parameters while simultaneously adjusting the model with neural network methods to improve accuracy when applied to real market data, especially in OTM options. The new hybrid model demonstrates superior accuracy compared to both traditional parametric and non-parametric option pricing models for both Call and Put options across all moneyness levels. The empirical results of the hybrid model provide an explanation for the deviation from the Put-Call parity observed in real market data.

Original languageEnglish
Article number123979
JournalExpert Systems with Applications
Volume251
DOIs
StatePublished - 1 Oct 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024 Elsevier Ltd

Keywords

  • Computational finance
  • Neural networks
  • Option pricing
  • Put-call parity

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