由连续半鞅驱动的倒向随机微分方程解的比较定理

彭实戈[1]首先建立了一维倒向随机微分方程的比较定理,本文在Lipschitz条件下研究由连续半鞅驱动的倒向随机微分方程,我们将比较定理推广到此类倒向随机微分方程,并且证明方法比彭实戈[1]的更加直接和简单.     

反射倒向随机微分方程的逆比较问题(Ⅰ)

在Briand,Coquet,Hu,Mémin,Peng[1],Coquet,Hu,Mémin,Peng[2],Chen[3],Jiang[8]等中,研究了倒向随机微分方程的逆比较定理,就是通过比较倒向随机微分方程的解来比较倒向随机微分方程的生成元问题.在文[9]中Li和Tang首次研究了反射倒向随机微分方程的逆比较问题.本文考虑在更一般的条件下,反射倒向随机微分方程的生成元的逆比较问题.     

反射倒向随机微分方程的逆比较问题(Ⅰ)

在Briand,Coquet,Hu,Memin,Peng[1],Coquet,Hu,Memin,Peng[2],Chen[3],Jiang [8]等中,研究了倒向随机微分方程的逆比较定理,就是通过比较倒向随机微分方程的解来比较倒向随机微分方程的生成元问题．在文[9]中Li和Tang首次研究了反射倒向随机微分方程的逆比较问题．本文考虑在更一般的条件下,反射倒向随机微分方程的生成元的逆比较问题．     

生成元连续且线性增长的反射倒向随机微分方程生成元的表示定理（英文）

本文建立了一个生成元满足连续且线性增长条件的反射倒向随机微分方程生成元的局部表示定理,此定理推广了一些已有的倒向随机微分方程生成元的表示定理.应用此表示定理,本文获得了一个一般的反射倒向随机微分方程的逆比较定理,同时讨论了此类方程的一些性质.     

正倒向随机微分方程解的比较定理

讨论了正倒向随机微分方程解的比较问题.阐述了正倒向随机微分方程在随机最优控制、现代金融理论中的广泛而深刻的应用, 对于一类正倒向随机微分方程, 利用Ito公式、停时等随机分析方法,通过构造辅助正倒向随机微分方程,得到了正倒向随机微分方程解的比较定理.     

一类倒向随机微分方程L~p解对终值的单调连续性

在生成元g满足关于y单调且关于z Lipschitz连续的条件下,范(2007)得到了倒向随机微分方程L~p解对终值的单调连续结果.在g关于y单调且关于z一致连续的条件下证明了倒向随机微分方程L~p解的单调连续性,推广了范(2007)的工作,并且方法是新的.     

由Lévy过程驱动的倒向双重随机微分方程的比较定理及其应用

本文研究了由Lévy过程和与之独立的布朗运动驱动的倒向双重随机微分方程,给出了相应的比较定理.作为比较定理的-个应用,文章证明了由Lévy过程驱动的倒向双重随机微分方程在其系数满足连续线性增长条件下解的存在性,并得到该方程的最小解.     

Lp空间中随机变量的g-期望与条件g-期望

利用倒向随机微分方程的Lp解定义了Lp空间中随机变量g-期望与条件g-期望,扩张了g-期望与条件g-期望的定义空间;证明了用Lp解定义的g-期望与文[5]用算子连续扩张方法定义的一般g-期望的一致性,得到了Lp空间中随机变量的g-期望与条件g-期望的一些性质.     

L~p空间中随机变量的g-期望与条件g-期望

利用倒向随机微分方程的Lp解定义了Lp空间中随机变量g-期望与条件g-期望,扩张了g-期望与条件g-期望的定义空间;证明了用Lp解定义的g-期望与文[5]用算子连续扩张方法定义的一般g-期望的一致性,得到了Lp空间中随机变量的g-期望与条件g-期望的一些性质.     

正倒向随机微分方程的适应解及其比较定理(英文)

研究了一类正倒向随机微分方程的适应解 ,其中正向方程不需要满足非退化条件 .我们证明了在某些单调条件下 ,正倒向随机微分方程存在唯一的适应解 ,并给出了该正倒向随机微分方程的比较定理 .     

Continuous dependence properties on solutions of backward stochastic differential equation

The existence theorem and continuous dependence property in ”L²” sense for solutions of backward stochastic differential equation (shortly BSDE) with Lipschitz coefficients were respectively established by Pardoux-Peng and Peng in [1,2], Mao and Cao generalized the Pardoux-Peng’s existence and uniqueness theorem to BSDE with non-Lipschitz coefficients in [3,4]. The present paper generalizes the Peng’s continuous dependence property in ”L²” sense to BSDE with Mao and Cao’s conditions. Furthermore, this paper investigates the continuous dependence property in “almost surely” sense for BSDE with Mao and Cao’s conditions, based on the comparison with the classical mathematical expectation.     

Backward SDEs with superquadratic growth

In this paper, we discuss the solvability of backward stochastic differential equations (BSDEs) with superquadratic generators. We first prove that given a superquadratic generator, there exists a bounded terminal value, such that the associated BSDE does not admit any bounded solution. On the other hand, we prove that if the superquadratic BSDE admits a bounded solution, then there exist infinitely many bounded solutions for this BSDE. Finally, we prove the existence of a solution for Markovian BSDEs where the terminal value is a bounded continuous function of a forward stochastic differential equation.     

Mean-field backward stochastic differential equations and related partial differential equations

In [R. Buckdahn, B. Djehiche, J. Li, S. Peng, Mean-field backward stochastic differential equations. A limit approach. Ann. Probab. (2007) (in press). Available online: http://www.imstat.org/aop/future_papers.htm] the authors obtained mean-field Backward Stochastic Differential Equations (BSDE) associated with a mean-field Stochastic Differential Equation (SDE) in a natural way as a limit of a high dimensional system of forward and backward SDEs, corresponding to a large number of “particles” (or “agents”). The objective of the present paper is to deepen the investigation of such mean-field BSDEs by studying them in a more general framework, with general coefficient, and to discuss comparison results for them. In a second step we are interested in Partial Differential Equations (PDE) whose solutions can be stochastically interpreted in terms of mean-field BSDEs. For this we study a mean-field BSDE in a Markovian framework, associated with a McKean–Vlasov forward equation. By combining classical BSDE methods, in particular that of “backward semigroups” introduced by Peng [S. Peng, J. Yan, S. Peng, S. Fang, L. Wu (Eds.), in: BSDE and Stochastic Optimizations; Topics in Stochastic Analysis, Science Press, Beijing (1997) (Chapter 2) (in Chinese)], with specific arguments for mean-field BSDEs, we prove that this mean-field BSDE gives the viscosity solution of a nonlocal PDE. The uniqueness of this viscosity solution is obtained for the space of continuous functions with polynomial growth. With the help of an example it is shown that for the nonlocal PDEs associated with mean-field BSDEs one cannot expect to have uniqueness in a larger space of continuous functions.     

一类非时齐非Lipschitz条件下带跳的倒向随机微分方程的适应解及比较定理

对系数f(t,y,z,k)满足非常一般的非时齐非Lipschitz条件,本文给出一类带跳的倒向随机微分方程局部和整体解的存在唯一性的证明,同时本文也研究了带跳的倒向随机微分方程的比较定理,从而把前人的相应结果推广到更一般情形．     

A numerical scheme to solve nonlinear bsdes with lipschitz and non-lipschitz coefficients

In this paper, we attempt to present a new numerical approach to solve non-linear backward stochastic differential equations. First, we present some definitions and theorems to obtain the conditions, from which we can approximate the non-linear term of the backward stochastic differential equation (BSDE) and we get a continuous piecewise linear BSDE correspond with the original BSDE. We use the relationship between backward stochastic differential equations and stochastic controls by interpreting BSDEs as some stochastic optimal control problems, to solve the approximated BSDE and we prove that the approximated solution converges to the exact solution of the original non-linear BSDE in two different cases.     

Higher order differentiability of solutions to backward stochastic differential equations

In this paper, we consider the differentiability in the sense of the Malliavin calculus of solutions to backward stochastic differential equations (BSDEs for short). It is known that a solution is differentiable in the sense of the Malliavin calculus and the derivative is also a solution to a linear BSDE. Under additional conditions, we will show that the higher order differentiability of a solution to a BSDE and that it also becomes a solution to a linear BSDE.     

BACKWARD STOCHASTIC DIFFERENTIAL EQUATION WITH RANDOM MEASURES

1. IntroductionPardoux and Peng[1], Peng[2'3] have discussed backward stochastic differential equations(BSDE) driven by Brownian motioll. Tangl4], Tang and Li[5] have considered BSDEdriven by Brownian motion and Poisson process. We will extend many results of them inthis paper.The main reference is [6].Let (fi, F, (R),P) be a filtered probability space, where the filtration (R) satisfies theusual conditions. Define (fi,F) ~ (fi x N x R,X x B(N) x B(R)),P ~ P x B(R), O =O x B(R),…     

Probabilistic approach to homogenization of a non-divergence form semilinear PDE with non-periodic coefficients

We consider a semilinear partial differential equation (PDE) of non-divergence form perturbed by a small parameter. We then study the asymptotic behavior of Sobolev solutions in the case where the coefficients admit limits in C?esaro sense. Neither periodicity nor ergodicity will be needed for the coefficients. In our situation, the limit (or averaged or effective) coefficients may have discontinuity. Our approach combines both probabilistic and PDEs arguments. The probabilistic one uses the weak convergence of solutions of backward stochastic differential equations (BSDE) in the Jakubowski S-topology, while the PDEs argument consists to built a solution, in a suitable Sobolev space, for the PDE limit. We finally show the existence and uniqueness for the associated averaged BSDE, then we deduce the uniqueness of the limit PDE from the uniqueness of the averaged BSDE.