Barrier Options and Greeks: Modeling with Neural Networks

Nneka Umeorah, Phillip Mashele, Onyecherelam Agbaeze, Jules Clement Mba

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


This paper proposes a non-parametric technique of option valuation and hedging. Here, we replicate the extended Black–Scholes pricing model for the exotic barrier options and their corresponding Greeks using the fully connected feed-forward neural network. Our methodology involves some benchmarking experiments, which result in an optimal neural network hyperparameter that effectively prices the barrier options and facilitates their option Greeks extraction. We compare the results from the optimal NN model to those produced by other machine learning models, such as the random forest and the polynomial regression; the output highlights the accuracy and the efficiency of our proposed methodology in this option pricing problem. The results equally show that the artificial neural network can effectively and accurately learn the extended Black–Scholes model from a given simulated dataset, and this concept can similarly be applied in the valuation of complex financial derivatives without analytical solutions.

Original languageEnglish
Article number384
Issue number4
Publication statusPublished - Apr 2023
Externally publishedYes


  • Black–Scholes model
  • artificial neural network
  • barrier options
  • data analysis
  • machine learning
  • option Greeks
  • polynomial regression
  • random forest regression

ASJC Scopus subject areas

  • Analysis
  • Algebra and Number Theory
  • Mathematical Physics
  • Logic
  • Geometry and Topology


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