Quantization-based approximation of reflected BSDEs with extended upper bounds for recursive quantization
arXiv:2105.07684
Abstract
We establish upper bounds for the -quantization error, p in (1, 2+d), induced by the recursive Markovian quantization of a d-dimensional diffusion discretized via the Euler scheme. We introduce a hybrid recursive quantization scheme, easier to implement in the high-dimensional framework, and establish upper bounds to the corresponding -quantization error. To take advantage of these extensions, we propose a time discretization scheme and a recursive quantization-based discretization scheme associated to a reflected Backward Stochastic Differential Equation and estimate -error bounds induced by the space approximation. We will explain how to numerically compute the solution of the reflected BSDE relying on the recursive quantization and compare it to other types of quantization.
arXiv comment40 pages, 1 figure, 3 tables
References in corpus (5)
- A forward--backward stochastic algorithm for quasi-linear PDEs
- Simulation of BSDEs by Wiener chaos expansion
- Numerical simulation of quadratic BSDEs
- Stratified regression Monte-Carlo scheme for semilinear PDEs and BSDEs with large scale parallelization on GPUs
- Approximation of backward stochastic differential equations using Malliavin weights and least-squares regression