Print Email Facebook Twitter An SGBM-XVA demonstrator Title An SGBM-XVA demonstrator: a scalable Python tool for pricing XVA Author Chau, K.W. (TU Delft Numerical Analysis) Tang, Jok (VORtech; TNO) Oosterlee, C.W. (TU Delft Numerical Analysis; Centrum Wiskunde & Informatica (CWI)) Date 2020-02-19 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. Subject CUDA PythonSGBMXVA To reference this document use: http://resolver.tudelft.nl/uuid:13d0cb7f-eb9c-4860-8b43-89c56f8ac98f DOI https://doi.org/10.1186/s13362-020-00073-5 ISSN 1612-3956 Source Mathematics in Industry, 10 (1) Part of collection Institutional Repository Document type journal article Rights © 2020 K.W. Chau, Jok Tang, C.W. Oosterlee Files PDF s13362_020_00073_5.pdf 1.66 MB Close viewer /islandora/object/uuid:13d0cb7f-eb9c-4860-8b43-89c56f8ac98f/datastream/OBJ/view