A Comparison of Artificial Neural Networks and Bootstrap Aggregating Ensembles in a Modern Financial Derivative Pricing Framework

Ryno du Plooy and Pierre J. Venter
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Ryno du Plooy: Department of Finance and Investment Management, University of Johannesburg, P.O. Box 524, Auckland Park 2006, South Africa
Pierre J. Venter: Department of Finance and Investment Management, University of Johannesburg, P.O. Box 524, Auckland Park 2006, South Africa

JRFM, 2021, vol. 14, issue 6, 1-18

Abstract: 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.

Keywords: artificial neural networks; vanilla option pricing; multi-curve framework; collateral; funding (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2021
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