英文标题：
《Deep Learning-Based Least Square Forward-Backward Stochastic
  Differential Equation Solver for High-Dimensional Derivative Pricing》
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作者：
Jian Liang and Zhe Xu and Peter Li
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最新提交年份：
2020
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英文摘要：
  We propose a new forward-backward stochastic differential equation solver for high-dimensional derivatives pricing problems by combining deep learning solver with least square regression technique widely used in the least square Monte Carlo method for the valuation of American options. Our numerical experiments demonstrate the efficiency and accuracy of our least square backward deep neural network solver and its capability to provide accurate prices for complex early exercise derivatives such as callable yield notes. Our method can serve as a generic numerical solver for pricing derivatives across various asset groups, in particular, as an efficient means for pricing high-dimensional derivatives with early exercises features.
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中文摘要：
我们将深度学习求解器与最小二乘蒙特卡罗方法中广泛使用的最小二乘回归技术相结合，提出了一种新的用于高维衍生品定价问题的正倒向随机微分方程求解器。我们的数值实验证明了我们的最小二乘后向深层神经网络解算器的效率和准确性，以及它能够为复杂的早期行使衍生工具（如可赎回收益率票据）提供准确的价格。我们的方法可以作为跨各种资产组的衍生工具定价的通用数值解算器，尤其是作为具有早期练习功能的高维衍生工具定价的有效手段。
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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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一级分类：Quantitative Finance        数量金融学
二级分类：Pricing of Securities        证券定价
分类描述：Valuation and hedging of financial securities, their derivatives, and structured products
金融证券及其衍生产品和结构化产品的估值和套期保值
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