ISSN: 2966-0599

v.1, n.6, 2024 (Outubro)

METADADOS

DOI:  10.69720/2966-0599.2024.0002

Author: Imoukhedeme Paul Kehinde

Biography: Helpman Development Institute

ORCID: https://orcid.org/0009-0008-1758-3237

E-mail: pimoukhedeme@helpmaninstitute.org

ABSTRACT:  This study examines the role of stochastic volatility in option pricing through the Heston model, employing advanced computational techniques such as Markov Chain Monte Carlo (MCMC) methods and Fourier transforms. By comparing the pricing accuracy of the Heston model, which captures stochastic volatility, to the Black- Scholes model, which assumes constant volatility, we demonstrate the superior flexibility and robustness of the Heston framework. Our results show that the Heston model consistently outperforms Black-Scholes, particularly when volatility parameters estimated via MCMC are well-calibrated, enabling more accurate representation of market dynamics. These findings underscore the importance of precise parameter estimation and advanced modeling techniques in improving option pricing accuracy.

Keywords: GRAPHICS, FOURIER TRANSFORM.