Approximating Option Greeks in a Classical and Multi-Curve Framework Using Artificial Neural Networks

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, 2024, vol. 17, issue 4, 1-26

Abstract: In this paper, the use of artificial neural networks (ANNs) is proposed to approximate the option price sensitivities of Johannesburg Stock Exchange (JSE) Top 40 European call options in a classical and a modern multi-curve framework. The ANNs were trained on artificially generated option price data given the illiquid nature of the South African market, and the out-of-sample performance of the optimized ANNs was evaluated using an implied volatility surface constructed from published volatility skews. The results from this paper show that ANNs trained on artificially generated input data are able to accurately approximate the explicit solutions to the respective option price sensitivities of both a classical and a modern multi-curve framework in a real-world out-of-sample application to the South African market.

Keywords: artificial neural networks; financial derivatives; Johannesburg Stock Exchange (JSE); machine learning; multi-curve framework; option price sensitivities (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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