A General Framework of Derivatives Pricing

Abstract

In this paper, we outline a general framework of derivatives pricing. The framework consists of two modules. The first is a novel simulation and machine learning based calibration module and the second one is a pricing module, which originates from [1] and [2]. Numerical examples show good applicability of the proposed framework. The methodology of calibration utilizes machine learning and simulation methods, combined, to deliver high quality parameter inference results and the pricing module is generic and can be applied to any financial derivatives. The machine learning based pricing methodologies can also generate prices on a future simulation grid, which facilitates XVA computations. Our methodologies can be applied to any pricing problem and the calibration routine is general and useful whenever a parametric model needs to be estimated.