GPU option pricing

Suo, Simon; Zhu, Rui; Attridge, Ryan; Wan, Jie

doi:10.1145/2830556.2830564

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…The American options, which were implemented using the Longstaff and Schwartz regression method, were supplied with a speedup that varied between 2× and 10× according to the number of generated paths, dimensions, and time discretization. Authors of [22] explored the calculation of individual options with OpenCL and CUDA, proposing several parallel implementations of the Lattice model and Monte Carlo numerical pricing methods. The parallel implementations achieved a significant performance improvement over serial implementations.…”

Section: Literature and Related Work 21 The Performance Of Option Pmentioning

confidence: 99%

Lower Precision calculation for option pricing

Ścibisz-Mordelska

Nielek

2017

csci

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The problem of option pricing is one of the most critical issues and fundamental building blocks in mathematical finance. The research includes the deployment of a lower-precision type in two option-pricing algorithms: the Black-Scholes and Monte Carlo simulations. We make an assumption that the shorter the number used for calculations is (in bits), the more operations we are able to perform at the same time. The results are examined by a comparison to the outputs of single-and double-precision types. The major goal of the study is to indicate whether the lower-precision types can be used in financial mathematics. The findings indicate that Black-Scholes provided more precise outputs than the basic implementation of the Monte Carlo simulation. Modification of the Monte Carlo algorithm is also proposed. The research shows the limitations and opportunities of the lower-precision type usage. In order to benefit from the application in terms of the time of calculation, the improved algorithms can be implemented on a GPU or FPGA. We conclude that, under particular restrictions, the lower-precision calculation can be used in mathematical finance.

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Section: Literature and Related Work 21 The Performance Of Option Pmentioning

confidence: 99%

Lower Precision calculation for option pricing

Ścibisz-Mordelska

Nielek

2017

csci

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“…Among the works done on GPU acceleration in option pricing, most have focused on numerical implementation on GPU based on lattice methods such as in Dai, et al [10] , Ganesan, et al [14] , Solomon, et al [15] , Zhang, et al [16] , Suo, et al [17] ; or based on Monte Carlo simulation such as in Michael and Françoise [18] , Podlozhnyuk and Mark [19] , Abbas-Turki and Bernard [20] , Trainor and Crookes [21] , Grauer-Gray, et al [22] and Yu, et al [23] ; or Fourier methods and partial differential approximations such as in Dang [24] , Dang, Christara and Jackson [25] , Surkov [4] and COS approach on GPU such as in Zhang and Oosterlee [26] . These implementations were done for a particular option pricing model and option type such as American, Asian, Basket, European or multi-asset options.…”

Section: Introductionmentioning

confidence: 99%

Parallelization and Acceleration of Dynamic Option Pricing Models on GPU-CPU Heterogeneous Systems

Wesley MUGANDA,

Shibwabo KASAMANI

2023

系统科学与信息学报(英文)

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In this paper, stochastic global optimization algorithms, specifically, genetic algorithm and simulated annealing are used for the problem of calibrating the dynamic option pricing model under stochastic volatility to market prices by adopting a hybrid programming approach. The performance of this dynamic option pricing model under the obtained optimal parameters is also discussed. To enhance the model throughput and reduce latency, a heterogeneous hybrid programming approach on GPU was adopted which emphasized a data-parallel implementation of the dynamic option pricing model on a GPU-based system. Kernel offloading to the GPU of the compute-intensive segments of the pricing algorithms was done in OpenCL. The GPU approach was found to significantly reduce latency by an optimum of 541 times faster than a parallel implementation approach on the CPU, reducing the computation time from 46.24 minutes to 5.12 seconds.

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A Comparison of Frameworks for Heterogeneous Computing Using High-Performance Pattern-Matching for DNA Biometrics and Digital Forensics

Stratan,

Pungila

2024

Lecture Notes in Networks and Systems

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GPU option pricing

Cited by 5 publications

References 4 publications

Lower Precision calculation for option pricing

Lower Precision calculation for option pricing

Parallelization and Acceleration of Dynamic Option Pricing Models on GPU-CPU Heterogeneous Systems

A Comparison of Frameworks for Heterogeneous Computing Using High-Performance Pattern-Matching for DNA Biometrics and Digital Forensics

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