American Options Pricing on Multi-core Graphic Cards

Abbas-Turki, Lokman; Lapeyre, Bernard

doi:10.1109/bife.2009.77

Cited by 23 publications

(20 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some work has been done in the acceleration of American options pricing on GPUs using Monte Carlo simulations, with Abbas-Turki and Lapeyre [12] reporting speedups of 4.8x to 8.7x on one GPU over a sequential CPU implementation for single asset American option, with GPU speedup increasing with the number of steps in the simulation paths. Joshi [13], on the pricing of Asian options, achieves a GPU speedup of 150x over a CPU using quasi-Monte Carlo simulation (which uses low discrepancy sequences instead of random numbers to increase speedup, but is dimension dependent).…”

Section: A Previous Work On Acceleration Of Pricing Enginesmentioning

confidence: 99%

An Environment for Rapid Derivatives Design and Experimentation

Crookes

Trainor

Jiang

2016

IEEE J. Sel. Top. Signal Process.

View full text Add to dashboard Cite

Section: A Previous Work On Acceleration Of Pricing Enginesmentioning

confidence: 99%

An Environment for Rapid Derivatives Design and Experimentation

Crookes

Trainor

Jiang

2016

IEEE J. Sel. Top. Signal Process.

View full text Add to dashboard Cite

“…Many acceleration strategies have been developed to solve option pricing problems on the GPU with different mathematical models [1,2,13]. For instance, Surkov [1] implements the FST method on a GPU architecture for option pricing; Abbas-Turki and Lapeyre [2] explore the performance of GPU on the American option pricing problem using the Longstaff and Schwartz method; Zhang and Oosterlee [13] perform option pricing with the COS method using the GPU.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, Surkov [1] implements the FST method on a GPU architecture for option pricing; Abbas-Turki and Lapeyre [2] explore the performance of GPU on the American option pricing problem using the Longstaff and Schwartz method; Zhang and Oosterlee [13] perform option pricing with the COS method using the GPU. However, little work among them is based on BSDEs.…”

Section: Related Workmentioning

confidence: 99%

Option Pricing on the GPU with Backward Stochastic Differential Equation

Peng¹,

Gong²,

Liu³

et al. 2011

2011 Fourth International Symposium on Parallel Architectures, Algorithms and Programming

View full text Add to dashboard Cite

Abstract-In this paper, we develop acceleration strategies for option pricing with non-linear Backward Stochastic Differential Equation (BSDE), which appears as a robust and valuable tool in financial markets. An efficient binomial lattice based method is adopted to solve the BSDE numerically. In order to reduce the global memory access frequency, the kernel invocation is avoided to be performed on each time step. Furthermore, for evaluating the affect of load balance to the performance, we provide two different acceleration strategies and compare them with running time experiments. The acceleration algorithms exhibit tremendous speedup over the sequential CPU implementation and therefore suitable for real-time application.

show abstract

“…in Equation (1). γ ∈ [0, 1] is a free parameter called the variance elasticity, and q δ and σ δ , δ = 1, 2, .…”

Section: Experiments With More General Approachmentioning

confidence: 99%

“…It is known that GPGPU is very well suitable for financial purposes and MC techniques. In the case of pricing early-exercise options, several contributions regarding graphics processing units (GPU) implementations of different techniques appeared recently [1,2,11,12,15,26]. All these papers are based on a combination of a MC method and dynamic programming, except the Dang et al paper, which uses partial differential equation (PDE) based pricing methods and the Pagès and Wilbertz paper, which employs MC simulation together with a quantization method.…”

Section: Introductionmentioning

confidence: 99%

GPU acceleration of the stochastic grid bundling method for early-exercise options

Leitao

Oosterlee

2015

International Journal of Computer Mathematics

View full text Add to dashboard Cite

In this work, a parallel graphics processing units (GPU) version of the Monte Carlo stochastic grid bundling method (SGBM) for pricing multi-dimensional early-exercise options is presented. To extend the method's applicability, the problem dimensions and the number of bundles will be increased drastically. This makes SGBM very expensive in terms of computational costs on conventional hardware systems based on central processing units. A parallelization strategy of the method is developed and the general purpose computing on graphics processing units paradigm is used to reduce the execution time. An improved technique for bundling asset paths, which is more efficient on parallel hardware is introduced. Thanks to the performance of the GPU version of SGBM, a general approach for computing the early-exercise policy is proposed. Comparisons between sequential and GPU parallel versions are presented.

show abstract

American Options Pricing on Multi-core Graphic Cards

Cited by 23 publications

References 10 publications

An Environment for Rapid Derivatives Design and Experimentation

An Environment for Rapid Derivatives Design and Experimentation

Option Pricing on the GPU with Backward Stochastic Differential Equation

GPU acceleration of the stochastic grid bundling method for early-exercise options

Contact Info

Product

Resources

About