Neural optimal stopping boundary

Andres Max Reppen, Halil Mete Soner, Valentin Tissot-Daguette

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

A method based on deep artificial neural networks and empirical risk minimization is developed to calculate the boundary separating the stopping and continuation regions in optimal stopping. The algorithm parameterizes the stopping boundary as the graph of a function and introduces relaxed stopping rules based on fuzzy boundaries to facilitate efficient optimization. Several financial instruments, some in high dimensions, are analyzed through this method, demonstrating its effectiveness. The existence of the stopping boundary is also proved under natural structural assumptions.

Original languageEnglish (US)
Pages (from-to)441-469
Number of pages29
JournalMathematical Finance
Volume35
Issue number2
DOIs
StatePublished - Apr 2025

All Science Journal Classification (ASJC) codes

  • Accounting
  • Finance
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Applied Mathematics

Keywords

  • American derivatives
  • Bermudan options
  • deep learning
  • fuzzy boundary
  • optimal stopping
  • stopping boundary problems

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