Sparse grid method for highly efficient computation of exposures for xVA

•Application of the sparse grid method significantly reduces the computational time for calculating exposures.•The stochastic collocation method significantly reduces the number of portfolio evaluations, even when dealing with many risk factors.•The proposed model can be easily applied to any portfolio and size, even for a portfolio comprising linear and non-linear derivatives.•The article gives illustrative examples and examines the method with realistic multi-currency portfolios consisting of interest rate swaps and swaptions. Every “x”-adjustment in the so-called xVA financial risk management framework relies on the computation of exposures. Considering thousands of Monte Carlo paths and tens of simulation steps, a financial portfolio needs to be evaluated numerous times during the lifetime of the underlying assets. This is the bottleneck of every simulation of xVA. In this article, we explore numerical techniques for improving the simulation of exposures. We aim to decimate the number of portfolio evaluations, particularly for large portfolios involving multiple, correlated risk factors. The usage of the Stochastic Collocation (SC) method Grzelak et al. (2019)[, together with Smolyak’s (1963), Judd et al. (2014) sparse grid extension, allows for a significant reduction in the number of portfolio evaluations, even when dealing with many risk factors. The proposed model can be easily applied to any portfolio and size.We report that for a realistic portfolio comprising linear and non-linear derivatives, the expected reduction in the portfolio evaluations may exceed 6000 times, depending on the dimensionality and the required accuracy. We give illustrative examples and examine the method with realistic multi-currency portfolios consisting of interest rate swaps and swaptions.