75 research outputs found
An SGBM-XVA demonstrator: A scalable Python tool for pricing XVA
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):2272â2301, 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
Rolling Adjoints: Fast Greeks along Monte Carlo scenarios for early-exercise options
In this paper we extend the stochastic grid bundling method (SGBM), a regress-later based Monte Carlo scheme for pricing early-exercise options, with an adjoint method to compute in a highly efficient manner sensitivities along the paths, with reasonable accuracy. With the ISDA standard initial margin model being adopted by the financial markets, computing sensitivities along scenarios is required to compute quantities like the margin valuation adjustment
Rolling Adjoints: Fast Greeks along Monte Carlo scenarios for early-exercise options
In this paper we extend the Stochastic Grid Bundling Method (SGBM), a regress-later Monte Carlo scheme for pricing early-exercise options, with an adjoint method to compute in a highly efficient manner the option sensitivities (the âGreeksâ) along the Monte Carlo paths, with reasonable accuracy. The path-wise SGBM Greeks computation is based on the conventional path-wise sensitivity analysis, however, for a regress-later technique. The resulting sensitivities at the end of the monitoring period are implicitly rolled over into the sensitivities of the regression coefficients of the previous monitoring date. For this reason, we name the method Rolling Adjoints, which facilitates Smoking Adjoints [M. Giles, P. Glasserman, Smoking adjoints: fast Monte Carlo Greeks, Risk 19 (1)(2006)88â92] to compute conditional sensitivities along the paths for options with early-exercise features
Stochastic grid bundling method for backward stochastic differential equations
In this work, we apply the Stochastic Grid Bundling Method (SGBM) to numerically solve backward stochastic differential equations. The SGBM algorithm is based on conditional expectations approximation by means of bundling of Monte Carlo sample paths and a local regress-later regression within each bundle. The basic algorithm for solving backward stochastic
GPU acceleration of the Seven-League Scheme for large time step simulations of stochastic differential equations
Monte Carlo simulation is widely used to numerically solve stochastic differential equations. Although the method is flexible and easy to implement, it may be slow to converge. Moreover, an inaccurate solution will result when using large time steps. The Seven League scheme, a deep learning-based numerical method, has been proposed to address these issues. This paper generalizes the scheme regarding parallel computing, particularly on Graphics Processing Units (GPUs), improving the computational speed
GeantV: Results from the prototype of concurrent vector particle transport simulation in HEP
Full detector simulation was among the largest CPU consumer in all CERN
experiment software stacks for the first two runs of the Large Hadron Collider
(LHC). In the early 2010's, the projections were that simulation demands would
scale linearly with luminosity increase, compensated only partially by an
increase of computing resources. The extension of fast simulation approaches to
more use cases, covering a larger fraction of the simulation budget, is only
part of the solution due to intrinsic precision limitations. The remainder
corresponds to speeding-up the simulation software by several factors, which is
out of reach using simple optimizations on the current code base. In this
context, the GeantV R&D project was launched, aiming to redesign the legacy
particle transport codes in order to make them benefit from fine-grained
parallelism features such as vectorization, but also from increased code and
data locality. This paper presents extensively the results and achievements of
this R&D, as well as the conclusions and lessons learnt from the beta
prototype.Comment: 34 pages, 26 figures, 24 table
- âŠ