英文标题：
《Solving Backward Stochastic Differential Equations with quadratic-growth
drivers by Connecting the Short-term Expansions》
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作者：
Masaaki Fujii, Akihiko Takahashi
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最新提交年份：
2018
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英文摘要：
This article proposes a new approximation scheme for quadratic-growth BSDEs in a Markovian setting by connecting a series of semi-analytic asymptotic expansions applied to short-time intervals. Although there remains a condition which needs to be checked a posteriori, one can avoid altogether time-consuming Monte Carlo simulation and other numerical integrations for estimating conditional expectations at each space-time node. Numerical examples of quadratic-growth as well as Lipschitz BSDEs suggest that the scheme works well even for large quadratic coefficients, and a fortiori for large Lipschitz constants.
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中文摘要：
本文通过连接一系列应用于短时间区间的半解析渐近展开式，提出了一种新的马尔可夫环境下二次增长盲源分离方程的近似方案。尽管仍有一个条件需要事后检查，但可以避免耗时的蒙特卡罗模拟和其他数值积分来估计每个时空节点的条件期望。二次增长和Lipschitz-BSDEs的数值例子表明，即使对于大的二次系数，该格式也能很好地工作，尤其是对于大的Lipschitz常数。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Quantitative Finance 数量金融学
二级分类：Mathematical Finance 数学金融学
分类描述：Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法，包括随机、概率和泛函分析、代数、几何和其他方法
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