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arXiv 1302.0679math.PR
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跳马氏过程的倒向随机微分方程及其应用

Backward stochastic differential equations associated to jump Markov processes and applications

Fulvia Confortola, Marco Fuhrman

摘要:

本文研究了一般状态空间K上的纯跳马尔可夫过程X的补偿随机测度驱动的倒向随机微分方程(BSDEs).应用这些结果证明了K上一类非线性抛物型微分方程的适定性,它推广了X上的Kolmogorov方程。最后,我们制定和解决马尔可夫跳过程的最优控制问题,有关的价值函数和最优控制法到一个适当的Bestival,也允许概率构造唯一的解决方案的Hamilton-Jacobi-Bellman方程,并确定它的价值函数。

英文摘要:

In this paper we study backward stochastic differential equations (BSDEs) driven by the compensated random measure associated to a given pure jump Markov process X on a general state space K. We apply these results to prove well-posedness of a class of nonlinear parabolic differential equations on K, that generalize the Kolmogorov equation of X. Finally we formulate and solve optimal control problems for Markov jump processes, relating the value function and the optimal control law to an appropriate BSDE that also allows to construct probabilistically the unique solution to the Hamilton-Jacobi-Bellman equation and to identify it with the value function.

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。

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