Neural Network for CVA: Learning Future Values

Abstract

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 [2, 3], 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. * Corporate Model Risk, Wells Fargo Bank, email contact: Jian-huang.She@wellsfargo.com † Corporate Model Risk, Wells Fargo Bank c 2018 Wells Fargo Bank, N. A. All rights reserved. The views expressed in this publication are our personal views and do not necessarily reflect the views of Wells Fargo Bank, N.A., its parent company, affiliates and subsidiaries.