On existence and uniqueness of solutions to uncertain backward stochastic differential equations

This paper is concerned with a class of uncertain backward stochastic differential equations （UBSDEs） driven by both an m-dimensional Brownian motion and a d-dimensional canonical process with uniform Lipschitzian coefficients. Such equations can be useful in mod- elling hybrid systems, where the phenomena are simultaneously subjected to two kinds of un- certainties： randomness and uncertainty. The solutions of UBSDEs are the uncertain stochastic processes. Thus, the existence and uniqueness of solutions to UBSDEs with Lipschitzian coeffi- cients are proved.

FULLY COUPLED FORWARD-BACKWARD STOCHASTIC DIFFERENTIAL EQUATIONS WITH GENERAL MARTINGALE

The article first studies the fully coupled Forward-Backward Stochastic Differential Equations （FBSDEs） with the continuous local martingale. The article is mainly divided into two parts. In the first part, it considers Backward Stochastic Differential Equations （BSDEs） with the continuous local martingale. Then, on the basis of it, in the second part it considers the fully coupled FBSDEs with the continuous local martingale. It is proved that their solutions exist and are unique under the monotonicity conditions.

A Mean-Field Stochastic Maximum Principle for Optimal Control of Forward-Backward Stochastic Differential Equations with Jumps via Malliavin Calculus

This paper considers a mean-field type stochastic control problem where the dynamics is governed by a forward and backward stochastic differential equation (SDE) driven by Lévy processes and the information available to the controller is possibly less than the overall information. All the system coefficients and the objective performance functional are allowed to be random, possibly non-Markovian. Malliavin calculus is employed to derive a maximum principle for the optimal control of such a system where the adjoint process is explicitly expressed.

Mean-Field Backward Stochastic Differential Equations Driven by Fractional Brownian Motion

In this paper,we study a new class of equations called mean-field backward stochastic differential equations(BSDEs,for short)driven by fractional Brownian motion with Hurst parameter H>1/2.First,the existence and uniqueness of this class of BSDEs are obtained.Second,a comparison theorem of the solutions is established.Third,as an application,we connect this class of BSDEs with a nonlocal partial differential equation(PDE,for short),and derive a relationship between the fractional mean-field BSDEs and PDEs.

On the speed of convergence of Picard iterations of backward stochastic differential equations

It is a well-established fact in the scientific literature that Picard iterations of backward stochastic differential equations with globally Lipschitz continuous nonlinearities converge at least exponentially fast to the solution.In this paper we prove that this convergence is in fact at least square-root factorially fast.We show for one example that no higher convergence speed is possible in general.Moreover,if the nonlinearity is zindependent,then the convergence is even factorially fast.Thus we reveal a phase transition in the speed of convergence of Picard iterations of backward stochastic differential equations.

Backward stochastic differential equations with rank-based data

In this paper, we investigate Markovian backward stochastic differential equations(BSDEs) with the generator and the terminal value that depend on the solutions of stochastic differential equations with rankbased drift coefficients. We study regularity properties of the solutions of this kind of BSDEs and establish their connection with semi-linear backward parabolic partial differential equations in simplex with Neumann boundary condition. As an application, we study the European option pricing problem with capital size based stock prices.

The Link between Stochastic Differential Equations with Non-Markovian Coefficients and Backward Stochastic Partial Differential Equations

In this paper, we conjecture and prove the link between stochastic differential equations with non-Markovian coefficients and nonlinear parabolic backward stochastic partial differential equations, which is an extension of such kind of link in Markovian framework to non-Markovian framework.Different from Markovian framework, where the corresponding partial differential equation is deterministic, the backward stochastic partial differential equation here has a pair of adapted solutions, and thus the link has a much different form. Moreover, two examples are given to demonstrate the applications of the derived link.

INVARIANT REPRESENTATION FOR STOCHASTIC DIFFERENTIAL OPERATOR BY BSDES WITH UNIFORMLY CONTINUOUS COEFFICIENTS AND ITS APPLICATIONS

In this paper, we prove that a kind of second order stochastic differential op- erator can be represented by the limit of solutions of BSDEs with uniformly continuous coefficients. This result is a generalization of the representation for the uniformly continuous generator. With the help of this representation, we obtain the corresponding converse comparison theorem for the BSDEs with uniformly continuous coefficients, and get some equivalent relationships between the properties of the generator g and the associated solutions of BSDEs. Moreover, we give a new proof about g-convexity.

UNIQUENESS OF VISCOSITY SOLUTIONS OF STOCHASTIC HAMILTON-JACOBI EQUATIONS

This article is devoted to the study of fully nonlinear stochastic Hamilton-Jacobi (HJ) equations for the optimal stochastic control problem of ordinary differential equations with random coefficients. Under the standard Lipschitz continuity assumptions on the coefficients, the value function is proved to be the unique viscosity solution of the associated stochastic HJ equation.

Backward doubly-stochastic differential equations with mean reflection

In this paper,we study a class of mean-reflected backward doubly stochastic differential equations(MR-BDSDEs),where the constraint depends on the law of the solution and not on its paths.The existence and uniqueness of these solutions were established.The penalization method plays an important role.We also provided a probabilistic interpretation of the classical solutions of the mean-reflected stochastic partial differential equations(MR-SPDEs)in terms of MR-BDSDEs.

SEMI-LINEAR SYSTEMS OF BACKWARD STOCHASTIC PARTIAL DIFFERENTIAL EQUATIONS IN R^n

This paper explores the diffeomorphism of a backward stochastic ordinary differential equation (BSDE) to a system of semi-linear backward stochastic partial differential equations (BSPDEs), under the inverse of a stochastic flow generated by an ordinary stochastic differential equation (SDE). The author develops a new approach to BSPDEs and also provides some new results. The adapted solution of BSPDEs in terms of those of SDEs and BSDEs is constructed. This brings a new insight on BSPDEs, and leads to a probabilistic approach. As a consequence, the existence, uniqueness, and regularity results are obtained for the (classical, Sobolev, and distributional) solution of BSPDEs.The dimension of the space variable x is allowed to be arbitrary n, and BSPDEs are allowed to be nonlinear in both unknown variables, which implies that the BSPDEs may be nonlinear in the gradient. Due to the limitation of space, however, this paper concerns only classical solution of BSPDEs under some more restricted assumptions.

Finite Element Methods for Nonlinear Backward Stochastic Partial Differential Equations and Their Error Estimates

In this paper,we consider numerical approximation of a class of nonlinear backward stochastic partial differential equations(BSPDEs).By using finite element methods in the physical space domain and the Euler method in the time domain,we propose a spatial finite element semi-discrete scheme and a spatio-temporal full discrete scheme for solving the BSPDEs.Errors of the schemes are rigorously analyzed and theoretical error estimates with convergence rates are obtained.

Forward and Backward Mean-Field Stochastic Partial Differential Equation and Optimal Control

This paper is mainly concerned with the solutions to both forward and backward mean-field stochastic partial differential equation and the corresponding optimal control problem for mean-field stochastic partial differential equation. The authors first prove the continuous dependence theorems of forward and backward mean-field stochastic partial differential equations and show the existence and uniqueness of solutions to them. Then they establish necessary and sufficient optimality conditions of the control problem in the form of Pontryagin’s maximum principles. To illustrate the theoretical results, the authors apply stochastic maximum principles to study the infinite-dimensional linear-quadratic control problem of mean-field type. Further, an application to a Cauchy problem for a controlled stochastic linear PDE of mean-field type is studied.

Stochastic Viscosity Solutions for SPDEs with Discontinuous Coefficients

In this paper, a class of nonlinear stochastic partial differential equations with discontinuous coefficients is investigated. This study is motivated by some research on stochastic viscosity solutions under non-Lipschitz conditions recently. By studying the solutions of backward doubly stochastic differential equations with discontinuous coefficients and constructing a new approximation function fn to the coefficient f, we get the existence of stochastic viscosity sub-solutions (or super-solutions).The results of this paper can be seen as the extension and application of related articles.

A New Representation for Second Order Stochastic Integral-differential Operators and Its Applications

In this paper, we＇ll prove new representation theorems for a kind of second order stochastic integral- differential operator by stochastic differential equations （SDEs） and backward stochastic differential equations （BSDEs） with jumps, and give some applications.

RBSDE's with jumps and the related obstacle problems for integral-partial differential equations

The author proves, when the noise is driven by a Brownian motion and an independent Poisson random measure, the one-dimensional reflected backward stochastic differential equation with a stopping time terminal has a unique solution. And in a Markovian framework, the solution can provide a probabilistic interpretation for the obstacle problem for the integral-partial differential equation.

THE MAXIMUM PRINCIPLE FOR PARTIALLY OBSERVED OPTIMAL CONTROL OF FORWARD-BACKWARD STOCHASTIC SYSTEMS WITH RANDOM JUMPS

这份报纸为被 Brownian 运动和独立泊松随机两个都驾驶的随机的系统测量的提交向后学习部分观察的最佳的控制的问题。联合提交向后有某些古典凸的变化技术的随机的微分方程理论，必要最大的原则为部分观察的最佳的控制被证明，在控制域是 nonempty 的地方凸的集合。在某些凸状假设下面，作者也为上述的最佳的最佳的问题给最佳的控制的足够的条件。说明理论结果，作者也得出部分信息的一个例子线性二次的最佳的控制，并且由使用必要、足够的最大的原则发现相应最佳的控制的明确的表情。

On Optimal Mean-Field Control Problem of Mean-Field Forward-Backward Stochastic System with Jumps Under Partial Information

This paper considers the problem of partially observed optimal control for forward-backward stochastic systems driven by Brownian motions and an independent Poisson random measure with a feature that the cost functional is of mean-field type. When the coefficients of the system and the objective performance functionals are allowed to be random, possibly non-Markovian, Malliavin calculus is employed to derive a maximum principle for the optimal control of such a system where the adjoint process is explicitly expressed. The authors also investigate the mean-field type optimal control problem for the system driven by mean-field type forward-backward stochastic differential equations(FBSDEs in short) with jumps, where the coefficients contain not only the state process but also its expectation under partially observed information. The maximum principle is established using convex variational technique. An example is given to illustrate the obtained results.

Maximum Principle for Partially-Observed Optimal Control Problems of Stochastic Delay Systems

This paper is concerned with partially-observed optimal control problems for stochastic delay systems. Combining Girsanov's theorem with a standard variational technique, the authors obtain a maximum principle on the assumption that the system equation contains time delay and the control domain is convex. The related adjoint processes are characterized as solutions to anticipated backward stochastic differential equations in finite-dimensional spaces. Then, the proposed theoretical result is applied to study partially-observed linear-quadratic optimal control problem for stochastic delay system and an explicit observable control variable is given.

正倒向随机微分方程理论基础及相关应用

本文从随机微分方程和倒向随机微分方程基本理论和应用背景谈起,结合随机最优控制理论和金融市场中的期权定价理论导出完全耦合的正倒向随机微分方程的形式.进而从该类方程的可解性这一角度出发,对已有的理论方法进行分析和探讨,引入一种非马尔科夫框架下保证解的存在唯一性的“统一框架”方法,给出比较定理、解的高维估计等重要性质,并联系相关偏微分方程系统给出其概率解释.对实际中应用广泛的线性正倒向随机微分方程引入了一种线性变换的方法作为“统一框架”方法的重要补充和完善,使得正倒向随机微分方程的应用更加广泛.