2023/11/18 by Josha A. Dekker, Dekker, Josha A., Roger J. A. Laeven +5
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Auction Theory and Applications #FOS: Mathematics #Optimization and Control (math.OC) #Optimization and Search Problems #Probability (math.PR) #Stochastic processes and financial applications
paper · pdf · doi:10.48550/arxiv.2311.11098
openalex publication_date 2023/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We develop methods to solve general optimal stopping problems with opportunities to stop that arrive randomly. Such problems occur naturally in applications with market frictions. Pivotal to our approach is that our methods operate on random rather than deterministic time scales. This enables us to convert the original problem into an equivalent discrete-time optimal stopping problem with ℕ0-valued stopping times and a possibly infinite horizon. To numerically solve this problem, we design a random times least squares Monte Carlo method. We also analyze an iterative policy improvement procedure in this setting. We illustrate the efficiency of our methods and the relevance of randomly arriving opportunities in a few examples.