A comparison of artificial neural networks and bootstrap aggregating ensembles in a modern financial derivative pricing framework

In this paper, the pricing performances of two learning networks, namely an artificial neural network and a bootstrap aggregating ensemble network, were compared when pricing the Johannesburg Stock Exchange (JSE) Top 40 European call options in a modern option pricing framework using a constructed i...

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Bibliographic Details
Published in:Journal of risk and financial management 2021-06, Vol.14 (6), p.1-18
Main Authors: Du Plooy, Ryno, Venter, Pierre J
Format: Article
Language:English
Subjects:
Agreements
artificial neural networks
Collateral
Credit risk
Default
Funding
Interest rates
Machine learning
Mechanics
multi-curve framework
Neural networks
Partial differential equations
Put & call options
Repurchase agreements
Research methodology
vanilla option pricing
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Summary:In this paper, the pricing performances of two learning networks, namely an artificial neural network and a bootstrap aggregating ensemble network, were compared when pricing the Johannesburg Stock Exchange (JSE) Top 40 European call options in a modern option pricing framework using a constructed implied volatility surface. In addition to this, the numerical accuracy of the better performing network was compared to a Monte Carlo simulation in a separate numerical experiment. It was found that the bootstrap aggregating ensemble network outperformed the artificial neural network and produced price estimates within the error bounds of a Monte Carlo simulation when pricing derivatives in a multi-curve framework setting.
ISSN:1911-8074
1911-8066
1911-8074
DOI:10.3390/jrfm14060254