An SGBM-XVA demonstrator: a scalable Python tool for pricing XVA

Ki Wai Chau*, Jok Tang, Cornelis W. Oosterlee

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

39 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number7
JournalMathematics in Industry
Volume10
Issue number1
DOIs
Publication statusPublished - 19 Feb 2020

Keywords

  • CUDA Python
  • SGBM
  • XVA

Fingerprint

Dive into the research topics of 'An SGBM-XVA demonstrator: a scalable Python tool for pricing XVA'. Together they form a unique fingerprint.

Cite this