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arXiv 2008.09429math.PR
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具有随机二次增长的双重反射倒向随机微分方程:关于可料障碍的研究

Doubly Reflected BSDEs With Stochastic Quadratic Growth: Around The Predictable Obstacles

E. H. Essaky, M. Hassani, C. Rhazlane

AI总结:

该研究在弱假设下证明了带可料障碍的随机二次增长广义双重反射RBSDE的最大/最小解存在性,用广义惩罚法得到等价形式与解的Snell包络刻画。

摘要:

我们证明了具有不规则障碍和随机二次增长的一维广义双重反射倒向随机微分方程(简称RBSDE)的最大(及最小)解的存在性;该方程的解$Y$必须在$[0; T[$区间内保持在两个右连左极(rcll)障碍$L$和$U$之间,且其左极限$Y_-$必须在$]0; T]$区间内分别保持在两个可料障碍$l$和$u$的上方与下方。我们在不假设任何$P$-可积条件、且对输入数据假设更弱的前提下完成了该证明。具体而言,当终端条件$\xi$仅为${\cal F}_T$-可测、驱动项$f$连续且关于变量$y$具有一般增长、关于变量$z$具有随机二次增长时,我们构造了此类RBSDE的最大解。我们的结果基于(广义)惩罚方法。该方法使我们能找到原RBSDE的等价形式,其解必须保持在两个新的右连左极反射障碍$\overline{Y}$和$\underline{Y}$之间;粗略来说,这两个障碍是由系数的控制条件所驱动的惩罚方程的极限。我们还给出了初始RBSDE的标准等价形式,以及将解$Y$刻画为某给定可料过程$l$的广义Snell包络的结论。

英文摘要:

We prove the existence of maximal (and minimal) solution for one-dimensional generalized doubly reflected backward stochastic differential equation (RBSDE for short) with irregular barriers and stochastic quadratic growth, for which the solution $Y$ has to remain between two rcll barriers $L$ and $U$ on $[0; T[$, and its left limit $Y_-$ has to stay respectively above and below two predictable barriers $l$ and $u$ on $]0; T]$. This is done without assuming any $P$-integrability conditions and under weaker assumptions on the input data. In particular, we construct a maximal solution for such a RBSDE when the terminal condition $ξ$ is only ${\cal F}_T-$measurable and the driver $f$ is continuous with general growth with respect to the variable $y$ and stochastic quadratic growth with respect to the variable $z$. Our result is based on a (generalized) penalization method. This method allow us find an equivalent form to our original RBSDE where its solution has to remain between two new rcll reflecting barriers $\overline{Y}$ and $\underline{Y}$ which are, roughly speaking, the limit of the penalizing equations driven by the dominating conditions assumed on the coefficients. A standard and equivalent form to our initial RBSDE as well as a characterization of the solution $Y$ as a generalized Snell envelope of some given predictable process $l$ are also given.

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