An SGBM-XVA demonstrator: a scalable Python tool for pricing XVA, 5 December 2018
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 [6], 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.
https://wakeupcall.project.cwi.nl/research-topics/papers/ki-wai-chau/xva.pdf/view
https://wakeupcall.project.cwi.nl/logo.png
An SGBM-XVA demonstrator: a scalable Python tool for pricing XVA, 5 December 2018
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 [6], 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.