Artificial Neural Networks Performance in WIG20 Index Options Pricing

Maciej Wysocki and Robert Ślepaczuk
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Maciej Wysocki: Quantitative Finance Research Group; Faculty of Economic Sciences, University of Warsaw

No 2020-19, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: In this paper the performance of artificial neural networks in option pricing is analyzed and compared with the results obtained from the Black – Scholes – Merton model based on the historical volatility. The results are compared based on various error metrics calculated separately between three moneyness ratios. The market data-driven approach is taken in order to train and test the neural network on the real-world data from the Warsaw Stock Exchange. The artificial neural network does not provide more accurate option prices. The Black – Scholes – Merton model turned out to be more precise and robust to various market conditions. In addition, the bias of the forecasts obtained from the neural network differs significantly between moneyness states.

Keywords: option pricing; machine learning; artificial neural networks; implied volatility; supervised learning; index options; Black – Scholes – Merton model (search for similar items in EconPapers)
JEL-codes: C14 C4 C45 C53 C58 G13 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2020
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-ore
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https://www.wne.uw.edu.pl/index.php/download_file/5722/ First version, 2020 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2020-19

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