英文标题：
《Neural Network for CVA: Learning Future Values》
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作者：
Jian-Huang She, and Dan Grecu
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最新提交年份：
2018
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英文摘要：
A new challenge to quantitative finance after the recent financial crisis is the study of credit valuation adjustment (CVA), which requires modeling of the future values of a portfolio. In this paper, following recent work in [Weinan E(2017), Han(2017)], we apply deep learning to attack this problem. The future values are parameterized by neural networks, and the parameters are then determined through optimization. Two concrete products are studied: Bermudan swaption and Mark-to-Market cross-currency swap. We obtain their expected positive/negative exposures, and further study the resulting functional form of future values. Such an approach represents a new framework for modeling XVA, and it also sheds new lights on other methods like American Monte Carlo.
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中文摘要：
在最近的金融危机之后，定量金融面临的一个新挑战是对信贷估值调整（CVA）的研究，这需要对投资组合的未来价值进行建模。在本文中，继[渭南E（2017），韩（2017）]最近的工作之后，我们应用深度学习来解决这个问题。通过神经网络对未来值进行参数化，然后通过优化确定参数。本文研究了两种具体产品：百慕大掉期期权和按市价交叉货币掉期。我们获得了他们预期的正/负风险敞口，并进一步研究了由此产生的未来价值函数形式。这种方法为XVA建模提供了一个新的框架，同时也为美国蒙特卡罗等其他方法带来了新的启示。
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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：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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