no code implementations • 19 Mar 2024 • Shige Peng, Shuzhen Yang, Wenqing Zhang
The integration and innovation of finance and technology have gradually transformed the financial system into a complex one.
no code implementations • 12 Apr 2022 • Shaolin Ji, Shige Peng, Ying Peng, Xichuan Zhang
In this paper, we mainly focus on the numerical solution of high-dimensional stochastic optimal control problem driven by fully-coupled forward-backward stochastic differential equations (FBSDEs in short) through deep learning.
no code implementations • 4 Nov 2021 • Shaolin Ji, Shige Peng, Ying Peng, Xichuan Zhang
In this paper, we mainly focus on solving high-dimensional stochastic Hamiltonian systems with boundary condition, which is essentially a Forward Backward Stochastic Differential Equation (FBSDE in short), and propose a novel method from the view of the stochastic control.
no code implementations • 18 Nov 2020 • Shige Peng, Shuzhen Yang
Based on law of large numbers and central limit theorem under nonlinear expectation, we introduce a new method of using G-normal distribution to measure financial risks.
1 code implementation • 5 Jul 2020 • Shaolin Ji, Shige Peng, Ying Peng, Xichuan Zhang
In this paper, we aim to solve the high dimensional stochastic optimal control problem from the view of the stochastic maximum principle via deep learning.
no code implementations • 11 Jul 2019 • Shaolin Ji, Shige Peng, Ying Peng, Xichuan Zhang
Recently, the deep learning method has been used for solving forward-backward stochastic differential equations (FBSDEs) and parabolic partial differential equations (PDEs).
no code implementations • 10 May 2018 • Shige Peng, Shuzhen Yang, Jianfeng Yao
Several well-established benchmark predictors exist for Value-at-Risk (VaR), a major instrument for financial risk management.