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 Reinforc
The development of sophisticated deep reinforcement learning techniques allows for significant advancements in solving complex optimal stopping problems that were previously intractable or suffered from discretization errors.
This research provides a more robust and flexible method for continuous-time optimal stopping, with broad implications for quantitative finance and other fields requiring precise decision-making under uncertainty.
The ability to learn exercise rules at arbitrarily fine time resolutions removes limitations of traditional discrete-time dynamic programming, improving accuracy and applicability in real-world scenarios.
- · Quantitative Finance Analysts
- · Financial Institutions
- · AI/ML Researchers
- · Hedging Desks
- · Traditional finance models reliant on coarse grids
- · Developers of less precise simulation methods
Financial models for options pricing and risk management become more accurate and efficient.
New financial products and strategies leveraging this enhanced precision could emerge, increasing market complexity.
The broader adoption of continuous-time DRL methods might accelerate AI integration across other sequential decision-making domains.
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Read at arXiv cs.LG