BSDEs的深度原对偶算法:机器学习在CVA和IM中的应用

ERN: Other Econometric Modeling: Capital Markets - Risk (Topic) Pub Date : 2017-11-15 DOI:10.2139/ssrn.3071506

P. Henry-Labordère

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引用次数: 70

摘要

在最近的nice论文[Weinan E-al(2017)]的基础上，我们引入了一种基于神经网络、随机梯度下降和随机控制问题的对偶公式的原始对偶方法来求解BSDEs。我们的算法用数学金融相关的两个例子来说明:交易对手风险定价和初始保证金计算。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Deep Primal-Dual Algorithm for BSDEs: Applications of Machine Learning to CVA and IM

Building heavily on the recent nice paper [Weinan E-al (2017)], we introduce a primal-dual method for solving BSDEs based on the use of neural networks, stochastic gradient descent and a dual formulation of stochastic control problems. Our algorithm is illustrated with two examples relevant in Mathematical Finance: the pricing of counterparty risk and the computation of initial margin.

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ERN: Other Econometric Modeling: Capital Markets - Risk (Topic)

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