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Option pricing and profitability: A comprehensive examination of machine learning, Black-Scholes, and Monte Carlo method
Communications for Statistical Applications and Methods 2024;31:585-599
Published online September 30, 2024
© 2024 Korean Statistical Society.

Sojin Kima, Jimin Kima, Jongwoo Song1,a

aDepartment of Statistics, Ewha Womans University, Korea
Correspondence to: 1 Department of Statistics, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Korea. E-mail: josong@ewha.ac.kr
Received April 1, 2024; Revised May 8, 2024; Accepted July 3, 2024.
 Abstract
Options pricing remains a critical aspect of finance, dominated by traditional models such as Black-Scholes and binomial tree. However, as market dynamics become more complex, numerical methods such as Monte Carlo simulation are accommodating uncertainty and offering promising alternatives. In this paper, we examine how effective different options pricing methods, from traditional models to machine learning algorithms, are at predicting KOSPI200 option prices and maximizing investment returns. Using a dataset of 2023, we compare the performance of models over different time frames and highlight the strengths and limitations of each model. In particular, we find that machine learning models are not as good at predicting prices as traditional models but are adept at identifying undervalued options and producing significant returns. Our findings challenge existing assumptions about the relationship between forecast accuracy and investment profitability and highlight the potential of advanced methods in exploring dynamic financial environments.
Keywords : option pricing, Black-Scholes model, MonteCarlo simulation, variance reduction, machine learning