10.57647/ijm2c.2026.160101

A New Numerical Method for solving delay Black-Scholes model

Received: 10-07-2025

Revised: 06-09-2025

Accepted: 20-09-2025

Published in Issue 31-03-2026

Published Online: 24-09-2025

How to Cite

Abdous, M., Vahidi, A., Damercheli, T., & Sedaghatfar, O. (2026). A New Numerical Method for solving delay Black-Scholes model. International Journal of Mathematical Modelling & Computations. https://doi.org/10.57647/ijm2c.2026.160101

Abstract

The objective of this study is to provide a numerical solution for stochastic delay differential equations, with a particular focus on the delay Black-Scholes model, utilizing the spectral collocation technique that employs radial basis functions. In this method, M-panels and r-point  Newton-Cotes integration was used to estimate the Ito integral. The main advantage of the proposed method is that it is easy to apply and leads to an algebraic equations system that is directly solved by numerical methods. Additionally, we analyze the stability and accuracy of the scheme through error estimation and comparisons with benchmark methods. To validate the approach, several numerical examples, including both linear and nonlinear SDDEs, are provided, demonstrating the method’s fast convergence and computational robustness. The results highlight the effectiveness of the spectral collocation approach in handling stochastic delays, offering a reliable framework for financial and engineering applications where randomness and delay play a critical role.

Keywords

  • Delay Black-Scholes model,
  • Spectral collocation technique,
  • Radial basis functions,
  • Newton-Cotes integration,
  • Ito integral

References

  1. Merton, R.c. (1991). Continuous-Time Finance, Oxford University Press. 4(4), 793-803.
  2. Rubinstein, M. (1994). Implied Binomial Trees. The Journal of Finance, 49(3), 771–818.
  3. Scott, L.O. (1987). Option pricing when the variance changes randomly: Theory, estimation, and an application. Journal of Financial and Quantitative Analysis, 22(4), 419–438. https://doi.org/10.2307/2330793.
  4. Vahidi, A. R., Babolian, E., Azimzadeh, Z. (2018). An Improvement to the Homotopy Perturbation Method for Solving Nonlinear Duffing’s Equations, Bulletin of the Malaysian Mathematical Sciences Society, 41(3), 1105–1117. 10.1007/s40840-015-0191-4.
  5. Vahidi, A. R., Azimzadeh, Z., Mohammadifar, S. (2012). Restarted Adomian Decomposition Method for Solving Duffing-van der Pol Equation. Applied Mathematical Sciences, 6(11), 499–507.https://www.researchgate.net/publication/265227996_
  6. Anh, V., Inoue, A. (2005). Financial markets with memory I: Dynamic models. Stochastic Analysis and Applications, 23(2),275300. https://www.researchgate.net/publication/27466029
  7. Arriojas, M., Hu, Y., Mohammed, S. E. A., Pap, G. (2007). A Delayed Black and Scholes Formula I. Stochastic Analysis and Applications, 25, 471–492. https://doi.org/10.48550/arXiv.math/0604640
  8. Higham, D. J. (2001). An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations. SIAM Review, 43(3), 525–546. https://doi.org/10.1137/S0036144500378302.
  9. Hu, Y., Mohammed, S. E. A., Yan, F. (2004). Discrete-time approximations of stochastic delay equations: the Milstein scheme. The Annals of Probability, 32(1A), 265–314. https://doi.org/10.1214/aop/1078415836.
  10. Stein, EM, Stein JC. 1991. Stock price distributions with stochastic volatility: an analytic approach. Rev Financ Stud. 4(4):727-52. https://www.jstor.org/stable/2962156.
  11. Shreve, SE. (2004). Stochastic Calculus for Finance II: Continuous-Time Models. New York: Springer.
  12. Shinde, AS, Takale KC. (2012). Study of Black-Scholes Model and its Applications. Procedia Eng. 38:270-9. https://doi.org/10.1016/j.proeng.2012.06.035
  13. Stoica, G. (2004). A stochastic delay financial model. Proc Am Math Soc. 133(6):1837-41. https://doi.org/10.1090/S0002-9939-04-07765-2.
  14. Zheng, Y. (2015). Asset Pricing Based on Stochastic Delay Differential Equations.
  15. Akhtari B, Babolian E, Foroush Bastani A. (2014). An adaptive weak continuous Euler-Maruyama method for stochastic delay differential equations. Numer Algorithms. 69(1):29-57. https://doi.org/10.1007/s11075-014-9880-6.
  16. Zong X, Wu F, Huang C. (2015). Exponential mean square stability of the theta approximations for neutral stochastic differential delay equations. J Comput Appl Math. 286:172-85. https://doi.org/10.1016/j.cam.2015.03.016.
  17. Banihashemi, S., Jafari, H., Babaei, A. (2022). A stable collocation approach to solve a neutral delay stochastic differential equation of fractional order. Journal of Computational and Applied Mathematics, 403, 113845. https://doi.org/10.1016/j.cam.2021.113845.
  18. Fodor, G., Sykora, H. T., & Bachrathy, D. (2023). Collocation method for stochastic delay differential equations. Probabilistic Engineering Mechanics, 74, 103515. https://doi.org/10.1016/j.probengmech.2023.103515.
  19. Ahmadi, N., Vahidi, A. R., & Allahviranloo, T. (2017). An efficient approach based on radial basis functions for solving stochastic fractional differential equations. Mathematical Sciences, 11(2), 113–118. https://doi.org/10.1007/s40096-017-0211-7.
  20. Kosec, G., Sarler, B. (2008). Local RBF Collocation Method for Darcy Flow. CMES: Computer Modeling in Engineering & Sciences, 25(3), 197–208. https://www.researchgate.net/publication/258396427_Local_RBF_Collocation_Method_for_Darcy_flow.
  21. Arezoomandan, M., Soheili, A. R. (2021). Spectral collocation method for stochastic partial differential equations with fractional Brownian motion. Journal of Computational and Applied Mathematics, 389, 113369. https://doi.org/10.1016/j.cam.2020.113369.
  22. Pettersson U, Larsson E, Marcusson G, Persson J. )2005 (Option pricing using radial basis functions. In: Proceedings of the ECCOMAS Thematic Conference on Meshless Methods; Nov 11-13; Lisbon, Portugal. https://www.researchgate.net/publication/247078464
  23. Mokhtari, S., Mesforush, A., Mokhtari, R., Akbari, R. (2024). An RBF-LOD Method for Solving Stochastic Diffusion Equations. Journal of Mathematics, 2024, Article ID 9955109. https://doi.org/10.1155/2024/9955109.
  24. Anco, S. C., Nualsaard, N., Luadsong, A., Aschariyaphotha, N. (2020). The Numerical Solution of Fractional Black-Scholes-Schrödinger Equation Using the RBFs Method. Advances in Mathematical Physics, 2020, 8868940. https://doi.org/10.1155/2020/1942762.
  25. Rad, J. A., Parand, K., Ballestra, L. V. (2015). Pricing European and American options by radial basis point interpolation. Applied Mathematics and Computation, 251, 363–377. https://doi.org/10.1016/j.amc.2014.11.016.
  26. Akhtari, B. (2019). Numerical solution of stochastic state-dependent delay differential equations: convergence and stability. Advances in Difference Equations, 2019(1), 396. https://doi.org/10.1186/s13662-019-2323-x.
  27. Gan, S., Yin, Z. (2015). Chebyshev spectral collocation method for stochastic delay differential equations. Advances in Difference Equations, 2015(1), 228. https://doi.org/10.1186/s13662-015-0447-1.
  28. Maleknejad, K., Ezzati, R., Damercheli, T. (2014). Solution of Multi-Delay Dynamic Systems by Using Hybrid Functions. Applied Mathematics, 5(13), 1969-1982. http://dx.doi.org/10.4236/am.2014.513194.
  29. Mao X.(2007). Stochastic Differential Equations and Applications. 2nd ed. Chichester: Horwood Publishing.
  30. Oksendal, B. (2000). Stochastic Differential Equations: An Introduction with Applications (5th ed.). Springer-Verlag.
  31. Khattak, A. J., Tirmizi, S. I. A., Islam, S. U. (2009). Application of meshfree collocation method to a class of nonlinear partial differential equations. Engineering Analysis with Boundary Elements, 33(5), 661-667. https://doi.org/10.1016/j.enganabound.2008.10.001.
  32. Delves, L. M., & Mohamed, J. L. (1988). Computational Methods for Integral Equations. Cambridge University Press.
  33. Phillips, G. M., & Taylor, P. J. (1996). Theory and Applications of Numerical Analysis (2nd ed.). Academic Press.