aiqbench analysis
This research introduces CARLOS, a reinforcement learning algorithm using aggregate deep neural networks to solve continuous-time optimal stopping problems without the discretization errors inherent in classical dynamic programming. By adaptively refining decision boundaries at arbitrary time resolutions, the method achieves higher valuation accuracy and computational efficiency compared to existing Bermudan solvers. The work represents a significant advancement in applying machine learning techniques to complex sequential decision-making tasks within quantitative finance.
Continuous-time Optimal Stopping through Deep Reinforcement Learning
arXiv:2606.17545v1 Announce Type: new Abstract: Simulation based solvers for optimal stopping problems must discretize the stopping decision. Under classical dynamic programming, a coarse exercise grid with only a few stopping opportunities can materially undervalue the optimal expected reward, whereas on a very fine grid, approximation errors accumulate through the backward recursion. To remove this limitation, we develop a new reinforcement-learning inspired algorithm that enables us to learn the exercise rule at arbitrarily fine time resolution. Our CARLOS (Continuous-time Adaptive Reinforcement Learning for Optimal Stopping) algorithm utilizes an aggregate deep neural network (ADNN) to learn a joint space-time decision boundary. Starting from a coarse time grid, we progressively increase the frequency of stopping opportunities, while in parallel training the ADNN to refine its timing-value estimates. We moreover design an adaptive sampling strategy that gradually concentrates training effort near the stopping boundary. Benchmarked results show that CARLOS delivers higher prices than existing Bermudan solvers, approaching the American upper bound, and achieves high computational efficiency relative to non-RL comparators.
Abstract:Simulation based solvers for optimal stopping problems must discretize the stopping decision. Under classical dynamic programming, a coarse exercise grid with only a few stopping opportunities can materially undervalue the optimal expected reward, whereas on a very fine grid, approximation errors accumulate through the backward recursion. To remove this limitation, we develop a new reinforcement-learning inspired algorithm that enables us to learn the exercise rule at arbitrarily fine time resolution. Our CARLOS (Continuous-time Adaptive Reinforcement Learning for Optimal Stopping) algorithm utilizes an aggregate deep neural network (ADNN) to learn a joint space-time decision boundary. Starting from a coarse time grid, we progressively increase the frequency of stopping opportunities, while in parallel training the ADNN to refine its timing-value estimates. We moreover design an adaptive sampling strategy that gradually concentrates training effort near the stopping boundary. Benchmarked results show that CARLOS delivers higher prices than existing Bermudan solvers, approaching the American upper bound, and achieves high computational efficiency relative to non-RL comparators.
Machine Learning (cs.LG); Computational Finance (q-fin.CP); Pricing of Securities (q-fin.PR)
arXiv:2606.17545 [cs.LG]
(or arXiv:2606.17545v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.17545 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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