Deep learning-based methods for deriving price bounds of American-style options and solving reflected BSDEs

This thesis is dedicated to mathematically evaluate a type of financial derivatives whose value depends on an optimal stopping time. We propose numerical methods based on the least squares Monte Carlo to solve this problem with the application of neural networks.