Option Pricing on the GPU with Backward Stochastic Differential Equation

Peng, Ying; Gong, Bin; Liu, Hui; Dai, Bin

doi:10.1109/paap.2011.12

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…For FBSDEs, Tran (2010) reconstructed the four step scheme with some new conditions, and combined it with the Schwarz waveform relaxation method to parallelize the system of quasilinear parabolic equations. Peng et al (2011) designed and implemented acceleration strategies on parallel GPUs for option pricing with BSDEs. Liu et al (2011) used X10's properties to implement option pricing with BSDEs.…”

Section: Other Methodsmentioning

confidence: 99%

Parallel Statistical Computing for Statistical Inference

Guo

2012

Journal of Statistical Theory and Practice

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Parallel statistical computing is an interesting and topical problem, driven by recent growth in the size of statistical data sets and the availability of network computing. This article reviews parallel statistical computing in regression analysis, nonparametric inference, and stochastic processes. In particular, we describe a range of methods including parallel multisplitting and the parallel QR method for least squares estimation in linear regression, parallel computing methods for nonlinear regression, the theoretical framework of the parallel bootstrap in nonparametric inference, preconditioner methods for Markov chains, and parallel Markov-chain Monte Carlo methods. We conclude that there is a need for further research in parallel statistical computing, and describe some of the important unsolved problems.

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Section: Other Methodsmentioning

confidence: 99%

Parallel Statistical Computing for Statistical Inference

Guo

2012

Journal of Statistical Theory and Practice

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“…Some acceleration strategies based on Graphics Processing Unit (GPU) computing have been developed for the pricing problems in finance, however, a very little of them are BSDE-based approach. These works can be found in [7,11,22], where the acceleration strategies are applied on numerical methods of convergence order not higher than 2.…”

Section: Introductionmentioning

confidence: 99%

Multistep schemes for solving backward stochastic differential equations on GPU

Kapllani

Teng

2022

J.Math.Industry

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The Backward Stochastic Differential Equation (BSDE) is an important tool for pricing and hedging. Highly accurate pricing for low computation time becomes interesting for minimizing monetary loss. Therefore, we explore the opportunity of parallelizing high-order multistep schemes in option pricing. In the multistep scheme the computations at each space grid point are independent and this fact motivates us to select massively parallel GPU computing using CUDA. In our investigations we identify performance bottlenecks and apply appropriate optimization techniques to reduce the computation time in a uniform space domain. Runtime experiments manifest optimistic speedups for the parallel implementation on a single GPU, NVIDIA GeForce 1070 Ti.

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“…The parallel program with CUDA achieved high speedups and showed that the GPU architecture is well suited for solving the BSDEs in parallel. Peng et al [Peng et al, 2011] developed acceleration strategies for option pricing with non-linear BSDEs using a binomial lattice based method. To increase the speedup, they reduce the global memory access frequency by avoiding the kernel invocation on each time step.…”

Section: Introductionmentioning

confidence: 99%

Multistep schemes for solving backward stochastic differential equations on GPU

Kapllani¹,

Li²

2019

Preprint

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The goal of this work is to parallelize the multistep scheme for solving the backward stochastic differential equations (BSDEs) in order to achieve both, a high accuracy and a reduction of the computation time as well. In the multistep scheme the computations at each grid point are independent and this fact motivates us to select massively parallel GPU computing using CUDA. In our investigations we identify performance bottlenecks and apply appropriate optimization techniques for reducing the computation time, using a uniform domain. Finally, some examples with financial applications are provided to demonstrate the achieved acceleration on GPUs.

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Option Pricing on the GPU with Backward Stochastic Differential Equation

Cited by 8 publications

References 11 publications

Parallel Statistical Computing for Statistical Inference

Parallel Statistical Computing for Statistical Inference

Multistep schemes for solving backward stochastic differential equations on GPU

Multistep schemes for solving backward stochastic differential equations on GPU

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