Numerical pricing of American options under two stochastic factor models with jumps using a meshless local Petrov-Galerkin method
Jamal Amani Rad, Kourosh Parand

TL;DR
This paper introduces a meshless local Petrov-Galerkin method using radial basis functions to efficiently and accurately price American options under complex stochastic models with jumps, avoiding traditional mesh-based limitations.
Contribution
It is the first application of meshless MLPG methods with WCS-RBFs to American option pricing under jump-diffusion models, demonstrating stability and high accuracy.
Findings
The proposed method is unconditionally stable.
Numerical results show high accuracy and computational efficiency.
Meshless approach avoids mesh dependency issues.
Abstract
The most recent update of financial option models is American options under stochastic volatility models with jumps in returns (SVJ) and stochastic volatility models with jumps in returns and volatility (SVCJ). To evaluate these options, mesh-based methods are applied in a number of papers but it is well-known that these methods depend strongly on the mesh properties which is the major disadvantage of them. Therefore, we propose the use of the meshless methods to solve the aforementioned options models, especially in this work we select and analyze one scheme of them, named local radial point interpolation (LRPI) based on Wendland's compactly supported radial basis functions (WCS-RBFs) with C6, C4 and C2 smoothness degrees. The LRPI method which is a special type of meshless local Petrov-Galerkin method (MLPG), offers several advantages over the mesh-based methods, nevertheless it has…
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