Barrier options and Greeks: Modeling with neural networks

Umeorah, Nneka ORCID: https://orcid.org/0000-0002-0307-5011, Mashele, Phillip, Agbaeze, Onyecherelam and Mba, Jules Clement 2023. Barrier options and Greeks: Modeling with neural networks. Axioms 12 (4) , 384. 10.3390/axioms12040384

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Abstract

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.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Mathematics
Additional Information:	License information from Publisher: LICENSE 1: URL: https://creativecommons.org/licenses/by/4.0/, Type: open-access
Publisher:	MDPI
Date of First Compliant Deposit:	9 May 2023
Date of Acceptance:	7 April 2023
Last Modified:	10 May 2023 13:42
URI:	https://orca.cardiff.ac.uk/id/eprint/159319

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