<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The present work proposes an Artificial Neural Network framework for calculating the price and delta hedging of American put option. We consider a sequence of Radial Basis function Neural Network, where each network learns the difference of the price function according to the Gaussian basis function. Based on Black Scholes partial differential equation, we improve the superiority of Artificial Neural Network by comparing the performance and the results achieved used in classic Monte Carlo Least Square simulation with those obtained by Neural networks in one dimension. Thus, numerical result shows that the Artificial Neural Network solver can reduce the computing time significantly as well as the error training.</p>
</abstract>

The present work proposes an Artificial Neural Network framework for calculating the price and delta hedging of American put option. We consider a sequence of Radial Basis function Neural Network, where each network learns the difference of the price function according to the Gaussian basis function. Based on Black Scholes partial differential equation, we improve the superiority of Artificial Neural Network by comparing the performance and the results achieved used in classic Monte Carlo Least Square simulation with those obtained by Neural networks in one dimension. Thus, numerical result shows that the Artificial Neural Network solver can reduce the computing time significantly as well as the error training.

Pricing American Put Option using RBF-NN: New Simulation of Black-Scholes

Engineering Sciences and Energy Management Laboratory

Faculty of Applied Sciences, Ibn Zohr University

Moroccan Journal of Pure and Applied Analysis

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Pricing American Put Option using RBF-NN: New Simulation of Black-Scholes"}

The present work proposes an Artificial Neural Network framework for calculating the price and delta hedging of American put option. We consider a sequence of...

Pricing American Put Option using RBF-NN: New Simulation of Black-Scholes

Pubblicato online: 13 gen 2022

Pagine: 78 - 91

Ricevuto: 21 dic 2020

Accettato: 17 giu 2021

DOI: https://doi.org/10.2478/mjpaa-2022-0007

Parole chiave
American option, Artificial neural network, Black-Scholes equation, Monte Carlo simulation, Option pricing, Radial basis function

© 2021 El Kharrazi Zaineb et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Pricing American Put Option using RBF-NN: New Simulation of Black-Scholes

Pubblicato online: 13 gen 2022

Pagine: 78 - 91

Ricevuto: 21 dic 2020

Accettato: 17 giu 2021

DOI: https://doi.org/10.2478/mjpaa-2022-0007

Parole chiaveAmerican option, Artificial neural network, Black-Scholes equation, Monte Carlo simulation, Option pricing, Radial basis function

© 2021 El Kharrazi Zaineb et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
American option, Artificial neural network, Black-Scholes equation, Monte Carlo simulation, Option pricing, Radial basis function