A novel local meshless scheme based on the radial basis function for pricing multi-asset options
Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
View: 175
This Paper With 10 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_CMDE-10-3_011
تاریخ نمایه سازی: 9 بهمن 1401
Abstract:
A novel local meshless scheme based on the radial basis function (RBF) is introduced in this article for price multi-asset options of even European and American types based on the Black-Scholes model. The proposed approach is obtained by using operator splitting and repeating the schemes of Richardson extrapolation in the time direction and coupling the RBF technology with a finite-difference (FD) method that leads to extremely sparse matrices in the spatial direction. Therefore, it is free of the ill-conditioned difficulties that are typical of the standard RBF approximation. We have used a strong iterative idea named the stabilized Bi-conjugate gradient process (BiCGSTAB) to solve highly sparse systems raised by the new approach. Moreover, based on a review performed in the current study, the presented scheme is unconditionally stable in the case of independent assets when spatial discretization nodes are equispaced. As seen in numerical experiments, it has a low computational cost and generates higher accuracy. Finally, the proposed local RBF scheme is very versatile so that it can be used easily for solving numerous models and obstacles not just in the finance sector, as well as in other fields of engineering and science.
Authors
Hamid Mesgarani
Department of Mathematics, Faculty of Science, Shahid Rajaee Teacher Training University, Tehran, ۱۶۷۸۵-۱۳۶, Iran.
Sara Ahanj
Department of Mathematics, Faculty of Science, Shahid Rajaee Teacher Training University, Tehran, ۱۶۷۸۵-۱۳۶, Iran.
Yones Esmaeelzade Aghdam
Department of Mathematics, Faculty of Science, Shahid Rajaee Teacher Training University, Tehran, ۱۶۷۸۵-۱۳۶, Iran.
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :