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Numerical Solutions for a Class of Backward Stochastic Differential Equations

机译:一类倒向随机微分方程的数值解

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We propose a method for numerical approximation of the solutions of backward stochastic differential equations in some non-lipschitz conditions for the coefficient functions and without the condition of the continuity for the final data. Given a simulation-based estimator of the conditional expectation operator, we then suggest a backward simulation scheme. Our explicitly method is simple to implement and it relies on approximation of Brownian motion by simple random walk.
机译:我们提出了一种在系数函数为非Lipschitz条件且无最终数据连续性的情况下,对倒向随机微分方程解进行数值逼近的方法。给定条件期望算子的基于仿真的估计量,然后我们提出一种反向仿真方案。我们的显式方法易于实现,它依赖于通过简单随机游走近似布朗运动。

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