RBF methods in a Stochastic Volatility framework for Greeks computation

In this paper we aim to apply a new, proposed meshless approach for Heston PDE resolution. In Mathematical Finance, most classical models may be reformulated as partial differential equations (PDE), which are commonly solved by finite difference and Monte Carlo methods. Alternatively, bio-inspired methods may be considered to select parameters and to solve said PDEs. In particular, we focus on Heston model PDE and we solve it via Radial Basis Functions (RBF) methods. Moreover some considerations on the computation of model parameters through learning approaches have been discussed. We aim to price a vanilla call option, focusing on the derivation of the formulae for its Greeks profiles. Eventually, we conclude RBFs are an accurate alternative method to price derivatives in a stochastic volatility framework, especially since they allow for a fast and precise computation of Greeks.