Research on Option Pricing Method Based on the Black-Scholes Model

Authors

  • Shang Xiang YAYATI LLC, Los Angeles, California, USA Author

DOI:

https://doi.org/10.71222/k8mkc798

Keywords:

Black-Scholes model, option pricing, stochastic volatility, jump-diffusion, financial engineering

Abstract

Option pricing is one of the core research topics in the field of financial engineering. As a classical option pricing method, the Black-Scholes model provides a significant foundation for both theory and practice in modern financial markets. This paper first elaborates on the theoretical foundation and mathematical derivation of the Black-Scholes model, analyzes its practical applications in option pricing, and explores its limitations, including the strict assumptions about market conditions and its applicability in environments with fluctuating volatility or sudden market jumps. To address these issues, this study improves the Black-Scholes model from two perspectives: stochastic volatility and jump-diffusion models. The improved models are validated through experimental designs, and their effectiveness is compared with the traditional model. The experimental results demonstrate that the improved models can provide more accurate pricing results in complex market environments. This research not only deepens the understanding of the Black-Scholes model but also offers new insights and approaches for pricing complex financial instruments.

References

1. R. Chowdhury et al., "Predicting the stock price of frontier markets using machine learning and modified Black–Scholes Option pricing model," Physica A: Stat. Mech. Appl., vol. 555, p. 124444, 2020, doi: 10.1016/j.physa.2020.124444.

2. S. Lin and X.-J. He, "A regime switching fractional Black–Scholes model and European option pricing," Commun. Nonlinear Sci. Numer. Simul., vol. 85, p. 105222, 2020, doi: 10.1016/j.cnsns.2020.105222.

3. X.-J. He and S. Lin, "A fractional Black-Scholes model with stochastic volatility and European option pricing," Expert Syst. Appl., vol. 178, p. 114983, 2021, doi: 10.1016/j.eswa.2021.114983.

4. P. Bajaj and J. Kaur, "Recent developments and applicability of the black and scholes model in option pricing: A literature review," MUDRA: J. Finance Account., vol. 7, no. 2, pp. 158-183, 2020, doi: 10.17492/jpi.mudra.v7i2.722034.

5. P. Morales-Bañuelos, N. Muriel, and G. Fernández-Anaya, "A modified Black-Scholes-Merton model for option pricing," Math., vol. 10, no. 9, p. 1492, 2022, doi: 10.3390/math10091492.

6. S. O. Edeki et al., "Coupled transform method for time-space fractional Black-Scholes option pricing model," Alexandria Eng. J., vol. 59, no. 5, pp. 3239-3246, 2020, doi: 10.1016/j.aej.2020.08.031.

7. L. Qian, J. Zhao, and Y. Ma, "Option pricing based on GA-BP neural network," Procedia Comput. Sci., vol. 199, pp. 1340-1354, 2022, doi: 10.1016/j.procs.2022.01.170.

8. M. Zhang and X. Zheng, "Numerical approximation to a variable-order time-fractional Black–Scholes model with applications in option pricing," Comput. Econ., vol. 62, no. 3, pp. 1155-1175, 2023, doi: 10.1007/s10614-022-10295-x.

9. P. Roul, "Design and analysis of a high order computational technique for time-fractional Black–Scholes model describing option pricing," Math. Methods Appl. Sci., vol. 45, no. 9, pp. 5592-5611, 2022, doi: 10.1002/mma.8130.

10. M. V. Klibanov et al., "Forecasting stock options prices via the solution of an ill-posed problem for the Black–Scholes equa-tion," Inverse Problems, vol. 38, no. 11, p. 115008, 2022, doi: 10.1088/1361-6420/ac91ec.

Downloads

Published

09 February 2025

Issue

Section

Article

How to Cite

Research on Option Pricing Method Based on the Black-Scholes Model. (2025). Economics and Management Innovation, 2(1), 69-77. https://doi.org/10.71222/k8mkc798