一般时间区间L~p-半鞅序列的单调极限定理

基于倒向随机微分方程(BSDE)和非线性期望理论中惩罚方法的启发,研究并得到了一般时间区间上L~p-半狹序列的单调极限定理.该结果的证明并非经典结果的平凡推广,新的框架让我们面对许多新问题,它将在一般框架下g-上鞅的Doob-Meyer型分解以及受限BSDE解的存在性等问题的探索中发挥重要作用.     

一类无穷区间的倒向随机微分方程及其应用

本文讨论了一类基于无穷区间的倒向随机微分方程解的存在唯一性及其性质. 由方程解定义一类非线性g-期望, 并讨论其在经济金融中的应用.     

具有一般跳过程的期权无差异效用价值过程的定价模型

本文采用指数效用最大化的方法研究了期权的动态无差异效用价值过程Ct(H;α).考虑股票价格过程为具有基于随机测度的一般跳的半鞅模型,且期权的无差异效用价值过程的Doob-Meyer分解的鞅部分的GKW(Galtchouk-Kunita-Watanabe)分解满足Jacod鞅表示定理.利用无差异效用价值过程在最小熵测度和最优投资策略下为鞅的事实构建了一个倒向随机微分方程.通过概率测度变换将方程的鞅部分和生成元转化为BMO(bounded mean oscillation)鞅,证明了该方程的解的唯一性.并将方程的生成元分成[?A=0]和[?A≠0],证明了最优投资策略存在.从而给出期权无差异效用价值过程的倒向随机微分方程的表达形式.     

带反射边界的倒向随机微分方程解的收敛性

对有界区间和无穷区间上带反射边界的倒向随机微分方程, 本文证明了其解的收敛性结果.     

非Lipschitz条件下g 上鞅的非线性Doob Meyer分解

作者讨论非Lipschitz条件下g 上鞅的非线性Doob Meyer 分解.　为此讨论一类漂移系数g(s,·,·)关于(y,z)不满足Lipschitz条 件的倒向随机微分方程解的存在唯一性,运用Biharis不等式证明了一类倒向随机微分方程的比较定理以及g 上解的极限定理.     

跳跃-扩散模型中期权定价的倒向随机微分方程方法及等价概率鞅测度

本文研究的是跳跃一扩散模型中的期权定价问题.通过研究该模型中未定权益所对应的倒向随机微分方程,找到市场中的-个等价概率鞅测度,借助测度变换,未定权益的定价问题就可转化为在等价概率鞅测度下的求期望问题.利用该方法,本文解得了标的股票价格过程为带非时齐:Poisson跳跃的扩散过程且股价期望增长率,波动率,无风险利率均为时间函数时欧式期权价格公式.并且,借助倒向随机微分方程找到在以上参数均为常数时,期权价格所满足的偏微分方程.     

半正定(半负定)二元函数基于g-期望的Jensen不等式

彭实戈通过倒向随机微分方程引入了g-期望的概念.在关于g-期望的最基本的条件下,提出并证明了:半正定(半负定)二元函数基于g-期望的Jensen不等式在非空数集S上成立当且仅当生成元g在S上是超线性(次线性)的.     

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.     

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.     

广义鞅及其收敛性

本文目的在于定义一种广义鞅并且讨论它的收敛性。文中首先给出了与某个增加的σ一代数族适应的随机序列几乎必然收敛的充分条件,推广了J.L.Doob的鞅收敛定理。然后讨论了广义鞅具有广义右闭元的条件以及它和几种收敛性之间的关系。最后叙述并且证明了广义鞅的Riesz型分解定理。     

Limit theorem and uniqueness theorem of backward stochastic differential equations

This paper establishes a limit theorem for solutions of backward stochastic differential equations (BSDEs). By this limit theorem, this paper proves that, under the standard assumption g(t,y,0) = 0, the generator g of a BSDE can be uniquely determined by the corresponding g-expectationεg;this paper also proves that if a filtration consistent expectation S can be represented as a g-expectationεg, then the corresponding generator g must be unique.     

Finite and infinite time interval BSDEs with non-Lipschitz coefficients

This paper aims at solving multidimensional backward stochastic differential equations (BSDEs) under weaker assumptions on the coefficients, considering both a finite and an infinite time interval. We establish a general existence and uniqueness result of the solutions to finite and infinite time interval BSDEs with non-Lipschitz coefficients, which generalizes the corresponding results in Mao (1995), Wang and Wang (2003), Wang and Huang (2009), Chen (1997) and Chen and Wang (2000).     

关于集值上鞅分解式的注记

讨论了集值上鞅与支撑函数的一些性质,利用支撑函数研究了一般Banach空间上集值上鞅的Riesz分解定理,推广和改进了以往的结果。     

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.     

One-dimensional BSDEs with finite and infinite time horizons

This paper is devoted to solving one-dimensional backward stochastic differential equations (BSDEs), where the time horizon may be finite or infinite and the assumptions on the generator g are not necessary to be uniform on t. We first show the existence of the minimal solution for this kind of BSDEs with linear growth generators. Then, we establish a general comparison theorem for solutions of this kind of BSDEs with weakly monotonic and uniformly continuous generators. Finally, we give an existence and uniqueness result for solutions of this kind of BSDEs with uniformly continuous generators.     

Quasi-martingales with a linearly ordered index set

We consider quasi-martingales indexed by a linearly order set. We show that such processes are isomorphic to a given class of (finitely additive) measures. From this result we easily derive the classical theorem of Stricker as well as the decompositions of Riesz, Rao and the supermartingale decomposition of Doob and Meyer.     

Progressive Enlargement of Filtrations and Backward Stochastic Differential Equations with Jumps

This work deals with backward stochastic differential equations (BSDEs for short) with random marked jumps, and their applications to default risk. We show that these BSDEs are linked with Brownian BSDEs through the decomposition of processes with respect to the progressive enlargement of filtrations. We prove that the equations have solutions if the associated Brownian BSDEs have solutions. We also provide a uniqueness theorem for BSDEs with jumps by giving a comparison theorem based on the comparison for Brownian BSDEs. We give in particular some results for quadratic BSDEs. As applications, we study the pricing and the hedging of a European option in a market with a single jump, and the utility maximization problem in an incomplete market with a finite number of jumps.     

Multidimensional BSDEs with weak monotonicity and general growth generators

This paper aims at solving a multidimensional backward stochastic differential equation （BSDE） whose generator g satisfies a weak monotonicity condition and a general growth condition in y. We first establish an existence and uniqueness result of solutions for this kind of BSDEs by using systematically the technique of the priori estimation, the convolution approach, the iteration, the truncation and the Bihari inequality. Then, we overview some assumptions related closely to the monotonieity condition in the literature and compare them in an effective way, which yields that our existence and uniqueness result really and truly unifies the Mao condition in y and the monotonieity condition with the general growth condition in y, and it generalizes some known results. Finally, we prove a stability theorem and a comparison theorem for this kind of BSDEs, which also improves some known results.     

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.