Discrete-time approximation and Monte-Carlo simulation of backward stochastic differential equations
Discrete-time approximation and Monte-Carlo simulation of backward stochastic differential equations
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DOI：
10.1016/j.spa.2004.01.001
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发表时间：
2004-06
影响因子：
1.4
通讯作者：
B. Bouchard;N. Touzi
中科院分区：
文献类型：
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作者：
B. Bouchard;N. Touzi
提出了一个解耦正倒向随机微分方程的离散时间近似。误差的Lp范数是时间步长的量级。给出了条件期望算子的一个基于模拟的估计量，然后提出了一个反向模拟方案，并研究了诱导的Lperror。这一估计是更多的调查的上下文中的Malliavin方法的近似条件期望。还考虑了对反射情况的扩展。
We suggest a discrete-time approximation for decoupled forward–backward stochastic differential equations. The Lpnorm of the error is shown to be of the order of the time step. Given a simulation-based estimator of the conditional expectation operator, we then suggest a backward simulation scheme, and we study the induced Lperror. This estimate is more investigated in the context of the Malliavin approach for the approximation of conditional expectations. Extensions to the reflected case are also considered.