A backward differential deep learning-based algorithm for solving high-dimensional nonlinear backward stochastic differential equations

By Lorenc Kapllani, Long Teng

Published 2024-04-12

Everscope rating

1769.5

Relevance to quantitative trading

8 / 10

Implementation complexity

7 / 10

Reproducibility

4 / 5

About this paper

Methodology: Differential Learning Backward Dynamic Programming (DLBDP). Problem types: Nonlinear Option Pricing, Hedging, High-Dimensional PDEs.

arXiv:2404.08456 · Paper rankings

Open the interactive Everscope explorer for full analysis, charts, and paper battles.