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

Ehsan Hajizadeh ()
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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

International Journal of Financial Engineering (IJFE), 2020, vol. 07, issue 03, 1-19

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.

Keywords: Option pricing; volatility; GARCH-type models; neural network; neuro-fuzzy network; particle swarm optimization algorithm (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S2424786320500255

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