Iterative Bregman Algorithm for Monte Carlo Enhancement

Sampled distributions are used to price, hedge and produce scenarios for derivative products which is essential for risk management. This approach accumulates many types of discretisation errors. These can be linked to data quality input, to calibration algorithms, discretisation in time, interpolation in space or even dependent on the sample’s size N. All these errors contribute in errors that are detrimental to the quality of the final result. We propose to work on the trajectories and smoothly transport them into a new version which is more appropriate to fit the target.The proposed method acts directly on the generated sample and is therefore model free