论文标题
大量金融衍生产品组合的CVA计算的深度学习
Deep learning for CVA computations of large portfolios of financial derivatives
论文作者
论文摘要
在本文中，我们提出了一种基于神经网络的方法，用于衍生产品组合的CVA计算。特别是，我们专注于由衍生物组合组合的投资组合，具有和不具有真正的选择性，\ textit {例如，}欧洲和百慕大型衍生物组合的投资组合。对于不同级别的WWR和交易对手的不同级别的信用质量，CVA计算出有或没有网的计算。我们表明，通过使用不调整交易对手默认风险的锻炼策略的标准程序，将CVA高估了25 \％。对于CVA动力学的预期缺口，在某些非极端情况下，高估被发现高出100 \％。
In this paper, we propose a neural network-based method for CVA computations of a portfolio of derivatives. In particular, we focus on portfolios consisting of a combination of derivatives, with and without true optionality, \textit{e.g.,} a portfolio of a mix of European- and Bermudan-type derivatives. CVA is computed, with and without netting, for different levels of WWR and for different levels of credit quality of the counterparty. We show that the CVA is overestimated with up to 25\% by using the standard procedure of not adjusting the exercise strategy for the default-risk of the counterparty. For the Expected Shortfall of the CVA dynamics, the overestimation was found to be more than 100\% in some non-extreme cases.
