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Developing an optimized artificial intelligence model for S&P 500 option pricing: A hybrid GARCH model

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  • Ehsan Hajizadeh

    (Department of Industrial Engineering and Management Systems, Amirkabir University of Technology (Tehran Polytechnics), 424 Hafez Ave., Tehran 15916-34311, Iran)

Abstract

In this paper, we propose two hybrid models to release some limitations and enhancement of the results. In this regard, three popular GARCH-type models are utilized for more accurate estimating of volatility, as the most important parameter for option pricing. Furthermore, the two non-parametric models based on Artificial Neural Networks and Neuro-Fuzzy Networks tuned by Particle Swarm Optimization algorithm are proposed to price call options for the S&P 500 index. By comparing the results obtained using these models, we conclude that both Neural Network and Neuro-Fuzzy Network models outperform the Black–Scholes model.

Suggested Citation

  • Ehsan Hajizadeh, 2020. "Developing an optimized artificial intelligence model for S&P 500 option pricing: A hybrid GARCH model," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 7(03), pages 1-19, September.
    • Handle: RePEc:wsi:ijfexx:v:07:y:2020:i:03:n:s2424786320500255
    DOI: 10.1142/S2424786320500255
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