Risk Optimisation: Finding the Signal in the Noise

Abstract

We introduce a new method of optimising the accuracy and time taken to calculate risk for a complex trading book, focusing on the use case of XVA. We dynamically choose the number of paths and time discretisation to target computational effort on calculations that give the most information in explaining the PnL of the book. The approach is applicable to both fast, accurate intraday pricing calculations as well as large batch runs. The results are demonstrated by application to a large XVA book, which demonstrates speed-ups comparable to those available via adjoint algorithmic differentiation, for a fraction of the implementation cost.