The Experts below are selected from a list of 41478 Experts worldwide ranked by ideXlab platform
Jian Liang - One of the best experts on this subject based on the ideXlab platform.
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deep learning based least squares forward backward Stochastic Differential Equation solver for high dimensional derivative pricing
Quantitative Finance, 2021Co-Authors: Jian LiangAbstract:We propose a new forward-backward Stochastic Differential Equation solver for high-dimensional derivative pricing problems by combining a deep learning solver with a least squares regression techni...
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deep learning based least square forward backward Stochastic Differential Equation solver for high dimensional derivative pricing
Social Science Research Network, 2020Co-Authors: Jian LiangAbstract:We propose a new forward-backward Stochastic Differential Equation solver for highdimensional derivative pricing problems by combining deep learning solver with least square regression technique widely used in the least square Monte Carlo method for the valuation of American options. Our numerical experiments demonstrate the accuracy of our least square backward deep neural network solver and its capability to produce accurate prices for complex early exercise derivatives, such as callable yield notes. Our method can serve as a generic numerical solver for pricing derivatives across various asset groups, in particular, as an accurate means for pricing high-dimensional derivatives with early exercise features.
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deep learning based least square forward backward Stochastic Differential Equation solver for high dimensional derivative pricing
arXiv: Computational Finance, 2019Co-Authors: Jian Liang, Zhe Xu, Peter LiAbstract:We propose a new forward-backward Stochastic Differential Equation solver for high-dimensional derivatives pricing problems by combining deep learning solver with least square regression technique widely used in the least square Monte Carlo method for the valuation of American options. Our numerical experiments demonstrate the efficiency and accuracy of our least square backward deep neural network solver and its capability to provide accurate prices for complex early exercise derivatives such as callable yield notes. Our method can serve as a generic numerical solver for pricing derivatives across various asset groups, in particular, as an efficient means for pricing high-dimensional derivatives with early exercises features.
Guangchen Wang - One of the best experts on this subject based on the ideXlab platform.
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a kind of lq non zero sum Differential game of backward Stochastic Differential Equation with asymmetric information
Automatica, 2018Co-Authors: Guangchen Wang, Hua Xiao, Jie XiongAbstract:Abstract This paper focuses on a kind of LQ non-zero sum Differential game driven by backward Stochastic Differential Equation with asymmetric information, which is a natural continuation of Wang and Yu (2010), Wang and Yu (2012). Different from Wang and Yu (2010) and Wang and Yu (2012), a realistic motivation for studying this kind of game is provided, and some feedback Nash equilibrium points are uniquely obtained by forward–backward Stochastic Differential Equations, their filters and the corresponding Riccati Equations with Markovian setting.
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an optimal control problem for mean field forward backward Stochastic Differential Equation with noisy observation
Automatica, 2017Co-Authors: Guangchen Wang, Hua Xiao, Guojing XingAbstract:Abstract This article is concerned with an optimal control problem derived by mean-field forward–backward Stochastic Differential Equation with noisy observation, where the drift coefficients of the state Equation and the observation Equation are linear with respect to the state and its expectation. The control problem is different from the existing literature concerning optimal control for mean-field Stochastic systems, and has more applications in mathematical finance, e.g., asset–liability management problem with recursive utility, systematic risk model. Using a backward separation method with a decomposition technique, two optimality conditions along with two coupled forward–backward optimal filters are derived. Linear–quadratic optimal control problems for mean-field forward–backward Stochastic Differential Equations are studied.
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an optimal control problem for mean field forward backward Stochastic Differential Equation with noisy observation
arXiv: Optimization and Control, 2015Co-Authors: Guangchen Wang, Hua Xiao, Guojing XingAbstract:This article is concerned with an optimal control problem derived by mean-field forward-backward Stochastic Differential Equation with noisy observation, where the drift coefficients of the state Equation and the observation Equation are linear with respect to the state and its expectation. The control problem is different from the existing literature on optimal control for mean-field Stochastic systems, and has more applications in mathematical finance, e.g., asset-liability management problem with recursive utility, systematic risk model. Using a backward separation method with a decomposition technique, two optimality conditions along with two coupled forward-backward optimal filters are derived. Several linear-quadratic optimal control problems for mean-field forward-backward Stochastic Differential Equations are studied. Closed-form optimal solutions are explicitly obtained in detailed situations.
Xinwei Feng - One of the best experts on this subject based on the ideXlab platform.
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reflected backward Stochastic Differential Equation with rank based data
Journal of Theoretical Probability, 2020Co-Authors: Zhenqing Chen, Xinwei FengAbstract:In this paper, we study reflected backward Stochastic Differential Equation (reflected BSDE) with rank-based data in a Markovian framework; that is, the solution to the reflected BSDE is above a prescribed boundary process in a minimal fashion and the generator and terminal value of the reflected BSDE depend on the solution of another Stochastic Differential Equation (SDE) with rank-based drift and diffusion coefficients. We derive regularity properties of the solution to such reflected BSDE and show that the solution at the initial starting time t and position x, which is a deterministic function, is the unique viscosity solution to some obstacle problem (or variational inequality) for the corresponding parabolic partial Differential Equation.
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reflected backward Stochastic Differential Equation with rank based data
arXiv: Probability, 2020Co-Authors: Zhenqing Chen, Xinwei FengAbstract:In this paper, we study reflected backward Stochastic Differential Equation (reflected BSDE in abbreviation) with rank-based data in a Markovian framework; that is, the solution to the reflected BSDE is above a prescribed boundary process in a minimal fashion and the generator and terminal value of the reflected BSDE depend on the solution of another Stochastic Differential Equation (SDE in abbreviation) with rank-based drift and diffusion coefficients. We derive regularity properties of the solution to such reflected BSDE, and show that the solution at the initial starting time $t$ and position $x$, which is a deterministic function, is the unique viscosity solution to some obstacle problem (or variational inequality) for the corresponding parabolic partial Differential Equation.
Guojing Xing - One of the best experts on this subject based on the ideXlab platform.
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an optimal control problem for mean field forward backward Stochastic Differential Equation with noisy observation
Automatica, 2017Co-Authors: Guangchen Wang, Hua Xiao, Guojing XingAbstract:Abstract This article is concerned with an optimal control problem derived by mean-field forward–backward Stochastic Differential Equation with noisy observation, where the drift coefficients of the state Equation and the observation Equation are linear with respect to the state and its expectation. The control problem is different from the existing literature concerning optimal control for mean-field Stochastic systems, and has more applications in mathematical finance, e.g., asset–liability management problem with recursive utility, systematic risk model. Using a backward separation method with a decomposition technique, two optimality conditions along with two coupled forward–backward optimal filters are derived. Linear–quadratic optimal control problems for mean-field forward–backward Stochastic Differential Equations are studied.
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an optimal control problem for mean field forward backward Stochastic Differential Equation with noisy observation
arXiv: Optimization and Control, 2015Co-Authors: Guangchen Wang, Hua Xiao, Guojing XingAbstract:This article is concerned with an optimal control problem derived by mean-field forward-backward Stochastic Differential Equation with noisy observation, where the drift coefficients of the state Equation and the observation Equation are linear with respect to the state and its expectation. The control problem is different from the existing literature on optimal control for mean-field Stochastic systems, and has more applications in mathematical finance, e.g., asset-liability management problem with recursive utility, systematic risk model. Using a backward separation method with a decomposition technique, two optimality conditions along with two coupled forward-backward optimal filters are derived. Several linear-quadratic optimal control problems for mean-field forward-backward Stochastic Differential Equations are studied. Closed-form optimal solutions are explicitly obtained in detailed situations.
Hua Xiao - One of the best experts on this subject based on the ideXlab platform.
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a kind of lq non zero sum Differential game of backward Stochastic Differential Equation with asymmetric information
Automatica, 2018Co-Authors: Guangchen Wang, Hua Xiao, Jie XiongAbstract:Abstract This paper focuses on a kind of LQ non-zero sum Differential game driven by backward Stochastic Differential Equation with asymmetric information, which is a natural continuation of Wang and Yu (2010), Wang and Yu (2012). Different from Wang and Yu (2010) and Wang and Yu (2012), a realistic motivation for studying this kind of game is provided, and some feedback Nash equilibrium points are uniquely obtained by forward–backward Stochastic Differential Equations, their filters and the corresponding Riccati Equations with Markovian setting.
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an optimal control problem for mean field forward backward Stochastic Differential Equation with noisy observation
Automatica, 2017Co-Authors: Guangchen Wang, Hua Xiao, Guojing XingAbstract:Abstract This article is concerned with an optimal control problem derived by mean-field forward–backward Stochastic Differential Equation with noisy observation, where the drift coefficients of the state Equation and the observation Equation are linear with respect to the state and its expectation. The control problem is different from the existing literature concerning optimal control for mean-field Stochastic systems, and has more applications in mathematical finance, e.g., asset–liability management problem with recursive utility, systematic risk model. Using a backward separation method with a decomposition technique, two optimality conditions along with two coupled forward–backward optimal filters are derived. Linear–quadratic optimal control problems for mean-field forward–backward Stochastic Differential Equations are studied.
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an optimal control problem for mean field forward backward Stochastic Differential Equation with noisy observation
arXiv: Optimization and Control, 2015Co-Authors: Guangchen Wang, Hua Xiao, Guojing XingAbstract:This article is concerned with an optimal control problem derived by mean-field forward-backward Stochastic Differential Equation with noisy observation, where the drift coefficients of the state Equation and the observation Equation are linear with respect to the state and its expectation. The control problem is different from the existing literature on optimal control for mean-field Stochastic systems, and has more applications in mathematical finance, e.g., asset-liability management problem with recursive utility, systematic risk model. Using a backward separation method with a decomposition technique, two optimality conditions along with two coupled forward-backward optimal filters are derived. Several linear-quadratic optimal control problems for mean-field forward-backward Stochastic Differential Equations are studied. Closed-form optimal solutions are explicitly obtained in detailed situations.