Journal of Computational Finance
ISSN:
1460-1559 (print)
1755-2850 (online)
Editor-in-chief: Christoph Reisinger
Numerical simulation and applications of the convection–diffusion–reaction equation with the radial basis function in a finite-difference mode
Need to know
- Localized meshfree techniques result in sparse matrices and with the added advantage of the RBF method can naturally handle irregular geometries.
- The goal of employing RBF-HFD method is to improve the accuracy of solution without increasing the stencil size which is the usual way to increase the accuracy in RBF-FD method.
- To overcome the discontinuity issue near the strike price for derivatives of pay off function in option pricing problem, an adaptive localized discretization is used for the spatial discretization.
Abstract
This paper develops two local mesh-free methods for designing stencil weights and spatial discretization, respectively, for parabolic partial differential equations (PDEs) of convection–diffusion–reaction type. These are known as the radial-basis-function generated finite-difference method and the Hermite finite-difference method. The convergence and stability of these schemes are investigated numerically using some examples in two and three dimensions with regularly and irregularly shaped domains. Then we consider the numerical pricing of European and American options under the Heston stochastic volatility model. The European option leads to the solution of a two-dimensional parabolic PDE, and the price of the American option is given by a linear complementarity problem with a two-dimensional parabolic PDE of convection–diffusion–reaction type. Then we use the operator splitting method to perform time-stepping after space discretization. The resulting linear systems of equations are well conditioned and sparse, and by numerical experiments we show that our numerical technique is fast and stable with respect to the change in the shape parameter of the radial basis function. Finally, numerical results are provided to illustrate the quality of approximation and to show how well our approach converges with the results presented in the literature.
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