An Explicit Scheme for Pathwise XVA Computations
arXiv:2401.13314
Abstract
Motivated by the equations of cross valuation adjustments (XVAs) in the realistic case where capital is deemed fungible as a source of funding for variation margin, we introduce a simulation/regression scheme for a class of anticipated BSDEs, where the coefficient entails a conditional expected shortfall of the martingale part of the solution. The scheme is explicit in time and uses neural network least-squares and quantile regressions for the embedded conditional expectations and expected shortfall computations. An a posteriori Monte Carlo validation procedure allows assessing the regression error of the scheme at each time step. The superiority of this scheme with respect to Picard iterations is illustrated in a high-dimensional and hybrid market/default risks XVA use-case.
References in corpus (4)
- Adam: A Method for Stochastic Optimization
- Deep learning-based numerical methods for high-dimensional parabolic partial differential equations and backward stochastic differential equations
- Numerical approximations of McKean Anticipative Backward Stochastic Differential Equations arising in Initial Margin requirements
- Statistical Learning of Value-at-Risk and Expected Shortfall