Forward deterministic pricing of options using Gaussian radial basis functions

A Quasi-radial Basis Functions Method for American Options Pricing

Option Pricing using Radial Basis Functions

In this paper, we have implemented a radial basis function (RBF) based method for solving the Black–Scholes partial differential equation. The application we have chosen is the valuation of European call options based on several underlying assets. We have shown that by appropriate choices of the RBF shape parameter and the node point placement, the accuracy of the results can be improved by at ...

A Comparison on Using Various Radial Basis Functions for Options Pricing

Stable Computations with Gaussian Radial Basis Functions

Radial basis function (RBF) approximation is an extremely powerful tool for representing smooth functions in non-trivial geometries, since the method is meshfree and can be spectrally accurate. A perceived practical obstacle is that the interpolation matrix becomes increasingly illconditioned as the RBF shape parameter becomes small, corresponding to flat RBFs. Two stable approaches that overco...

Gaussian Radial Basis Functions for Simulation Metamodeling

This paper presents a novel approach for developing simulation metamodels using Gaussian radial basis functions. This approach is based on some recently developed mathematical results for radial basis functions. It is systematic, explicitly controls the underfitting and overfitting tradeoff, and uses a fast computational algorithm that requires minimal human involvement. This approach is illust...