In this work, we developed a Python demonstrator for pricing total valuation adjustment (XVA) based on the stochastic grid bundling method (SGBM). XVA is an advanced risk management concept which became relevant after the recent financial crisis. This work is a follow-up work on Chau and Oosterlee in (Int J Comput Math 96(11):2272–2301, 2019), in which we extended SGBM to numerically solving backward stochastic differential equations (BSDEs). The motivation for this work is basically two-fold. On the application side, by focusing on a particular financial application of BSDEs, we can show the potential of using SGBM on a real-world risk management problem. On the implementation side, we explore the potential of developing a simple yet highly efficient code with SGBM by incorporating CUDA Python into our program.

中文翻译：

---

SGBM-XVA演示器：用于对XVA定价的可扩展Python工具

在这项工作中，我们基于随机网格捆绑方法（SGBM）开发了用于定价总估值调整（XVA）的Python演示程序。XVA是一种先进的风险管理概念，在最近的金融危机之后变得很重要。这项工作是Chau和Oosterlee在（Int J Comput Math 96（11）：2272–2301，2019）中的后续工作，其中我们将SGBM扩展为以数值方式求解反向随机微分方程（BSDE）。这项工作的动机基本上有两个方面。在应用程序方面，通过关注BSDE的特定财务应用程序，我们可以展示在实际的风险管理问题上使用SGBM的潜力。在实现方面，我们探索了通过将CUDA Python集成到我们的程序中来使用SGBM开发简单而高效的代码的潜力。