Search Results for author: Shige Peng

Found 7 papers, 1 papers with code

Uncertainty in the financial market and application to forecastabnormal financial fluctuations

no code implementations19 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.

A deep learning method for solving stochastic optimal control problems driven by fully-coupled FBSDEs

no code implementations12 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.

A novel control method for solving high-dimensional Hamiltonian systems through deep neural networks

no code implementations4 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.

Distributional uncertainty of the financial time series measured by G-expectation

no code implementations18 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.

Time Series Time Series Analysis

Solving stochastic optimal control problem via stochastic maximum principle with deep learning method

1 code implementation5 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.

Three algorithms for solving high-dimensional fully-coupled FBSDEs through deep learning

no code implementations11 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).

Improving Value-at-Risk prediction under model uncertainty

no code implementations10 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.

Management

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