arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1701.02899math.PR
收藏

不含驱动鞅的倒向随机微分方程、马氏过程及其伪偏微分方程

Backward Stochastic Differential Equations with no driving martingale, Markov processes and associated Pseudo Partial Differential Equations

Adrien Barrasso, Francesco Russo

摘要:

讨论了一类不含驱动鞅的倒向随机微分方程。当驱动程序的随机性取决于一个一般的马尔可夫过程$X$,这些BSDES被命名为马尔可夫BSDES,并可以与一个确定性的问题,称为伪PDE构成的parabolicsemilinear PDE的自然推广,自然出现时,底层的过滤是布朗。我们考虑了伪偏微分方程的两个适定性:“经典”解和“鞅”解。

英文摘要:

We discuss a class of Backward Stochastic Differential Equations(BSDEs) with no driving martingale. When the randomness of the driver depends on a general Markov process $X$, those BSDEs are denominated Markovian BSDEs and can be associated to a deterministic problem,called Pseudo-PDE which constitute the natural generalization of a parabolicsemilinear PDE which naturally appears when the underlying filtration is Brownian. We consider two aspects of well-posedness forthe Pseudo-PDEs: "classical" and "martingale" solutions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑