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Deep Learning for Optimal Stopping

This project replicates and extends the deep learning framework of Becker et al. (2020) for solving optimal stopping problems, including:

  • Pricing Bermudan max-call options
  • Callable multi-barrier reverse convertibles (MBRCs)
  • Stopping fractional Brownian motion

We benchmark results against the Longstaff–Schwartz algorithm.

Authors

Sergio Leal, Lana Popović, Pablo Pastor, Matthieu Fisher

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