Solving optimal stopping problems with Deep Q-Learning

arXiv:2101.09682v2 Announce Type: replace-cross
Abstract: We propose a reinforcement learning (RL) approach to model optimal exercise strategies for option-type products. We pursue the RL avenue in order to learn the optimal action-value function of the underlying stopping problem. In addition to retrieving the optimal Q-function at any time step, one can also price the contract at inception. We first discuss the standard setting with one exercise right, and later extend this framework to the case of multiple stopping opportunities in …

abstract action arxiv exercise function learn price problem products q-fin.pr q-learning reinforcement reinforcement learning replace stat.ml strategies type value