Affiliations: [a] Department of Statistics, Faculty of Mathematical Science and Computer, Allameh Tabataba’i University, Tehran, Iran | [b] Department of Mathematics and Statistics, University of Montreal, Montreal, QC, Canada
Correspondence:
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Corresponding author: Abdolrahim Badamchizadeh, Department of Statistics, Faculty of Mathematical Science and Computer, Allameh Tabataba’i University, Tehran, Iran. Tel.: +98 9122095843; E-mail: Badamchi@atu.ac.ir.
Abstract: Implied volatility modeling is the future anticipation of price fluctuation and so has a crucial role in option pricing. Machine learning approach can be applied as a great tool to modeling implied volatility and predicting the corresponding future data working towards improving the validity of final outcomes. Usualy, the majority of traders and investors are willing to be encountered with a simple model which is easy to understand, so we provide a light method to reach the goal. In this paper, we propose a machine learning polynomial approach due to the smile shaped behavior of implied volatility and investigate it with a regularization penalty term to fit the Out-The-Money volatility data and we compare the result with the prominent counterpart SVI. Finally, the promising numerical results illustrate that the new proposed algorithm yields an implied volatility smile which is free from static arbitrage for Out-The-Money European call options most of the time and it outperforms SVI in prediction.
