Randomized optimal stopping algorithms and their convergence analysis
Optimization and Control
2020-02-05 v1 Numerical Analysis
Numerical Analysis
Computational Finance
Abstract
In this paper we study randomized optimal stopping problems and consider corresponding forward and backward Monte Carlo based optimisation algorithms. In particular we prove the convergence of the proposed algorithms and derive the corresponding convergence rates.
Cite
@article{arxiv.2002.00816,
title = {Randomized optimal stopping algorithms and their convergence analysis},
author = {Christian Bayer and Denis Belomestny and Paul Hager and Paolo Pigato and John Schoenmakers},
journal= {arXiv preprint arXiv:2002.00816},
year = {2020}
}