随机游走和离散的倒向随机微分方程

本文研究了随机游走和离散的倒向随机微分方程。把随机游走到布朗运动的收敛推广到L^2情形；而且根据倒向随机微分方程的理论框架研究了离散的倒向随机微分方程，得到了离散的倒向随机微分方程解的存在唯一性和比较定理，这实际上给出了倒向随机微分方程的一种离散方法，为理论和实际研究提供了方便。     

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.     

倒向随机微分方程弱解（英）

引入倒向随机微分方程弱解的概念，应用Girsanov变换，建立了两类倒向随机微分方程（0．1）和（0．2）弱解存在的等价性，由此得到倒向 随机微分方程弱解存在的几个充分条件。     

正倒向随机微分方程理论及应用

正倒向随机微分方程源于随机控制和金融等问题的研究,反之,方程理论的研究成果在控制、金融等领域也有着重要的应用。基于正向和倒向随机微分方程的理论成果,正倒向随机微分方程的研究在短时间内取得了长足进步。本文将从方程可解性这一角度出发,对正倒向随机微分方程目前取得的成果进行系统的总结与探讨。     

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,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．     

正倒向随机微分方程的适应解及其比较定理

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。     

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.     

高维倒向随机微分方程比较定理

倒向随机微分方程由Pardoux和彭实戈首先提出，彭实戈给出了一维BSDE的比较定理，周海滨将其推广到了高维情形．毛学荣将倒向随机微分方程解的存在唯一性定理推广到非Lipschitz系数情况，曹志刚和严加安给了相应的一维比较定理．本文将曹志刚和严加安的比较定理推广到高维情形．     

数学金融学中的若干问题

近年来,数学金融学受到越来越多的人的重视。本文将粗略地介绍数学金融学中的几个有趣且重要的问题,例如,期权定价,统一公债问题等等。研究这些问题的一个有力工具是倒向随机微分方程和正倒向随机微分方程。我们将简明扼要地介绍倒向和正倒向随机微分方程在数学金融学的研究中所起的作用。特别地,我们将用它们导出著名的Black—Scholes期权定价公式。     

A link of stochastic differential equations to nonlinear parabolic equations

Using Girsanov transformation,we derive a new link from stochastic differential equations of Markovian type to nonlinear parabolic equations of Burgers-KPZ type,in such a manner that the obtained BurgersKPZ equation characterizes the path-independence property of the density process of Girsanov transformation for the stochastic differential equation.Our assertion also holds for SDEs on a connected differential manifold.     

The interrelation between stochastic differential inclusions and set-valued stochastic differential equations

In this paper we connect the well established theory of stochastic differential inclusions with a new theory of set-valued stochastic differential equations. Solutions to the latter equations are understood as continuous mappings taking on their values in the hyperspace of nonempty, bounded, convex and closed subsets of the space L²$L^2$ consisting of square integrable random vectors. We show that for the solution X$X$ to a set-valued stochastic differential equation corresponding to a stochastic differential inclusion, there exists a solution x$x$ for this inclusion that is a _‖⋅‖L2${\Vert \cdot \Vert }_{L^2}$-continuous selection of X$X$. This result enables us to draw inferences about the reachable sets of solutions for stochastic differential inclusions, as well as to consider the viability problem for stochastic differential inclusions.     

An Infinite-Dimensional Fractional Linear Quadratic Regulator Problem

We consider a controlled linear stochastic infinite-dimensional differential equation with an additive fractional Brownian motion as noise input. An optimal closed-loop control is determined in the case of complete state information and a quadratic goal functional.     

HAMILTON-JACOBI-BELLMAN方程的混合元方法

0　引　　言考虑下列随机微分方程 :ｄＸｔｄｔ =σ(Ｘｔ,αｔ) ζｔ+ｂ(Ｘｔ,αｔ)　ｔ≥ 0 ( 1 .1ａ)　　　　Ｘ0 =ｘ ∈ＲＮ ( 1 .1ｂ)　　此处 ,σ和ｂ分别是定义在ＲＮ×Ａ上的矩阵值和向量值函数 ,Ａ是给定的可分空间 ,αｔ值位于Ａ中的随机过程 更严格地 ,方程 ( 1 .1ａ)可写成下列形式 :Ｘｔ =ｘ + ∫ｔ0 σ(Ｘｓ,αｓ)ｄＢｓ+ ∫ｔ0 ｂ(Ｘｓ,αｓ)ｄｓ　ｔ≥ 0 ( 1 .2 )此外 ,Ｂｔ 是ｍ 维Ｂｒｏｗｎ运动 ,使得 :Ｅ(Ｂｔ) =0 ,Ｅ(Ｂ2 ｔ) =ｔ;∫ｔ0 σ(Ｘｓ,αｓ)ｄＢｓ 是随机积分 我们知道 ,σ ,ｂ若足够光滑 ,则保证了对每一…     

带随机跳跃的线性二次非零和微分对策问题

对于一类以布朗运动和泊松过程为噪声源的正倒向随机微分方程,在单调性假设下,给出了解的存在性和唯一性的结果.然后将这些结果应用于带随机跳跃的线性二次非零和微分对策问题之中,由上述正倒向随机微分方程的解得到了开环Nash均衡点的显式形式.     

Mean-field stochastic control with elephant memory in finite and infinite time horizon

ABSTRACT

Our purpose of this paper is to study stochastic control problems for systems driven by mean-field stochastic differential equations with elephant memory, in the sense that the system (like the elephants) never forgets its history. We study both the finite horizon case and the infinite time horizon case.

• In the finite horizon case, results about existence and uniqueness of solutions of such a system are given. Moreover, we prove sufficient as well as necessary stochastic maximum principles for the optimal control of such systems. We apply our results to solve a mean-field linear quadratic control problem.

• For infinite horizon, we derive sufficient and necessary maximum principles.

  As an illustration, we solve an optimal consumption problem from a cash flow modelled by an elephant memory mean-field system.

     

On the generalized 2-D stochastic Ginzburg-Landau equation

This paper proves the existence and uniqueness of solutions in a Banach space for the generalized stochastic Ginzburg-Landau equation with a multiplicative noise in two spatial dimensions. The noise is white in time and correlated in spatial variables. The condition on the parameters is the same as in the deterministic case. The Banach contraction principle and stochastic estimates in Banach spaces are used as the main tool.     

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.