Fast and Efficient Numerical Methods for an Extended Black-Scholes Model
This work provides incremental improvements in computational efficiency for solving PIDEs in financial modeling, but does not introduce a new paradigm or achieve broad SOTA results.
The paper develops efficient numerical methods for solving a partial integro-differential equation (PIDE) arising in option pricing, using wavelet and Fourier sine basis preconditioners and multigrid methods to accelerate iterative solvers, and analyzes the stability and accuracy of two one-step schemes.
An efficient linear solver plays an important role while solving partial differential equations (PDEs) and partial integro-differential equations (PIDEs) type mathematical models. In most cases, the efficiency depends on the stability and accuracy of the numerical scheme considered. In this article we consider a PIDE that arises in option pricing theory (financial problems) as well as in various scientific modeling and deal with two different topics. In the first part of the article, we study several iterative techniques (preconditioned) for the PIDE model. A wavelet basis and a Fourier sine basis have been used to design various preconditioners to improve the convergence criteria of iterative solvers. We implement a multigrid (MG) iterative method. In fact, we approximate the problem using a finite difference scheme, then implement a few preconditioned Krylov subspace methods as well as a MG method to speed up the computation. Then, in the second part in this study, we analyze the stability and the accuracy of two different one step schemes to approximate the model.