Parallel Option Pricing with BSDE Method on GPU

Dai, Bin; Peng, Ying; Gong, Bin

doi:10.1109/gcc.2010.47

Cited by 12 publications

(10 citation statements)

References 12 publications

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“…Dai et al (2010) used GPUs to accelerate option pricing computations with BSDEs. They showed that when the number of time steps and Monte Carlo simulations are small, the speed-ups seem insignificant, but that higher accuracy will be achieved when these numbers are large.…”

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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“…To get the solution of the BSDEs, Dai et al (2010) used GPU to speed up option pricing computations with the BSDEs. It is shown that, when the numbers of simulations are small, the speedups seem insignificant.…”

Section: Block Allocation For the Bsders In Financementioning

confidence: 99%

Finite Difference Methods for the BSDEs in Finance

Guo

2018

IJFS

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This paper gives a review of numerical methods for solving the BSDEs, especially, finite difference methods. For numerical methods of finite difference, we should divide them into three branches. Distributed method (or parallel method) should now become a hot topic. It is a key reason we present the review. We give a brief survey on the financial problems. The problems include solution and simulation methods for the BSDEs. We first describe the BSDEs, and then outline the main techniques and main results of the BSDEs. In addition, we compare with the errors between these methods and the Euler method on the BSDEs.

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“…A GPU-based (graphics processing unit) solution [Dai et al 2010] to the BSDE approach for option pricing was presented by the same group of researchers, where they adopted the theta method [Zhao et al 2006] to solve BSDEs. (The theta method discretises a continuous BSDE on a time-space grid.…”

Section: Related Workmentioning

confidence: 99%

“…The implementations were tested on a 2.67 GHz Intel Core i7 920 and an NVIDIA Tesla C1060. When N = 128 the runtime of the sequential code was about 23000 seconds, and that of the GPU code was about 99 seconds ( Table 1 in [Dai et al 2010])! Zubair and Mukkamala [2008] proposed a cache-friendly parallel option pricing algorithm for shared memory symmetric multi-processors (SMP).…”

Section: Related Workmentioning

confidence: 99%

Parallel Binomial American Option Pricing under Proportional Transaction Costs

Zhang

Roux

Zastawniak

2012

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We present a parallel algorithm that computes the ask and bid prices of an American option when proportional transaction costs apply to trading in the underlying asset. The algorithm computes the prices on recombining binomial trees, and is designed for modern multi-core processors. Although parallel option pricing has been well studied, none of the existing approaches takes transaction costs into consideration. The algorithm that we propose partitions a binomial tree into blocks. In any round of computation a block is further partitioned into regions which are assigned to distinct processors. To minimise load imbalance the assignment of nodes to processors is dynamically adjusted before each new round starts. Synchronisation is required both within a round and between two successive rounds. The parallel speedup of the algorithm is proportional to the number of processors used. The parallel algorithm was implemented in C/C++ via POSIX Threads, and was tested on a machine with 8 processors. In the pricing of an American put option, the parallel speedup against an efficient sequential implementation was 5.26 using 8 processors and 1500 time steps, achieving a parallel efficiency of 65.75%

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Parallel Option Pricing with BSDE Method on GPU

Cited by 12 publications

References 12 publications

Parallel Statistical Computing for Statistical Inference

Parallel Statistical Computing for Statistical Inference

Finite Difference Methods for the BSDEs in Finance

Parallel Binomial American Option Pricing under Proportional Transaction Costs

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