JOURNAL ARTICLE

Adaptive multi-asset Black–Scholes option pricing model and COMSOL implementation.

  • Published In: International Journal of Modeling, Simulation & Scientific Computing, 2026, v. 17, n. 2. P. 1 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Yu, Ruo-Xi 3 of 3

Abstract

Accurate valuation of options, among the most significant financial derivatives, is critical for the stability and efficiency of the derivatives market. This paper presents a practical and reproducible implementation workflow for solving multi-asset Black–Scholes (BS) pricing partial differential equations (PDEs) using adaptive finite elements in COMSOL Multiphysics. The backward differentiation formula (BDF) is adopted for the time discretization of the BS equation. The COMSOL built-in adaptive module is employed to improve the computational accuracy of the numerical solutions. Two refinement indicators and four boundary conditions are investigated through benchmark problems. The feature in COMSOL, i.e., Livelink for MATLAB, is employed to call the well-established functions of MATLAB and conveniently evaluate cumulative normal distribution required by certain boundary conditions. Through simulating the cash-or-nothing call option and European put option, the accuracy and robustness of the proposed method are validated. Detailed implementation in COMSOL is given, so it is convenient for readers or researchers without programming experience to employ the proposed method to solve the multi-asset BS equation. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modeling, Simulation & Scientific Computing. 2026/04, Vol. 17, Issue 2, p1
  • Document Type:Article
  • Subject Area:Mathematics
  • Publication Date:2026
  • ISSN:17939623
  • DOI:10.1142/S179396232650008X
  • Accession Number:193365045
  • Copyright Statement:Copyright of International Journal of Modeling, Simulation & Scientific Computing is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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