Abstract
In this work, we apply the Stochastic Grid Bundling Method (SGBM) to numerically solve backward stochastic differential equations (BSDEs). The SGBM algorithm is based on conditional expectations approximation by means of bundling of Monte Carlo sample paths and a local regress-later regression within each bundle. The basic algorithm for solving the backward stochastic differential equations will be introduced and an upper error bound is established for the local regression. A full error analysis is also conducted for the explicit version of our algorithm and numerical experiments are performed to demonstrate various properties of our algorithm.
| Original language | English |
|---|---|
| Pages (from-to) | 2272-2301 |
| Number of pages | 30 |
| Journal | International Journal of Computer Mathematics |
| Volume | 96 |
| Issue number | 11 |
| DOIs | |
| Publication status | Published - 2 Nov 2019 |
| Externally published | Yes |
Bibliographical note
Funding Information:This work is supported by EU Framework Programme for Research and Innovation Horizon 2020 (H2020-MSCA-ITN-2014, Project 643045, ‘EID WAKEUPCALL’). The authors would like to thank VORtech, BV, for their help and advice for this work and the anonymous reviewers for their valuable advice for improving this work.
Publisher Copyright:
© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.
Funding
This work is supported by EU Framework Programme for Research and Innovation Horizon 2020 (H2020-MSCA-ITN-2014, Project 643045, ‘EID WAKEUPCALL’). The authors would like to thank VORtech, BV, for their help and advice for this work and the anonymous reviewers for their valuable advice for improving this work.
Keywords
- BSDE
- bundling
- Monte-Carlo
- regress-later
- SGBM