5 papers
Particle Methods with Deep Learning for Stochastic Control under Partial Observation
Mathieu Laurière, Xiaolu Tan, Jiefei Yang
Numerical computation of stochastic control problems under partial observation is challenging because the dynamic programming formulation is naturally posed on the conditional dist…
Dual Approaches to Stochastic Control via SPDEs and the Pathwise Hopf Formula
Mathieu Laurière, Jiefei Yang
We develop dual approaches for continuous-time stochastic control problems, enabling the computation of robust dual bounds in high-dimensional state and control spaces. Building on…
A deep primal-dual BSDE method for optimal stopping problems
Jiefei Yang, Guanglian Li
We present a new deep primal-dual backward stochastic differential equation framework based on stopping time iteration to solve optimal stopping problems. A novel loss function is…
Gradient-enhanced sparse Hermite polynomial expansions for pricing and hedging high-dimensional American options
Jiefei Yang, Guanglian Li
We propose an efficient and easy-to-implement gradient-enhanced least squares Monte Carlo method for computing price and Greeks (i.e., derivatives of the price function) of high-di…
On Sparse Grid Interpolation for American Option Pricing with Multiple Underlying Assets
Jiefei Yang, Guanglian Li
In this work, we develop a novel efficient quadrature and sparse grid based polynomial interpolation method to price American options with multiple underlying assets. The approach…