12796
Robin Kumar, Amandeep Kaur Cheema
Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. Neural network is the well-known branch of machine learning & it has been used extensively by researchers for prediction of data and the prediction accuracy depends upon fine tuning of particular financial data. In this paper […]
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Chuan-Hsiang Han, Yu-Tuan Lin
Monte Carlo simulations have become widely used in computational finance. Standard error (SE in short) is the basic notion to measure the quality of a Monte Carlo estimator, and the square of SE is defined as the variance divided by the total number of simulations. Variance reduction methods have been developed as efficient algorithms by […]
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Linlin Xu, Giray Okten
GPU computing has become popular in computational finance and many financial institutions are moving their CPU based applications to the GPU platform. Since most Monte Carlo algorithms are embarrassingly parallel, they benefit greatly from parallel implementations, and consequently Monte Carlo has become a focal point in GPU computing. GPU speed-up examples reported in the literature […]
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Gordon Inggs, David Thomas, Wayne Luk
We advocate a domain specific software development methodology for heterogeneous computing platforms such as Multicore CPUs, GPUs and FPGAs. We argue that three specific benefits are realised from adopting such an approach: portable, efficient implementations across heterogeneous platforms; domain specific metrics of quality that characterise platforms in a form software developers will understand; automatic, optimal […]
Rida Assaf
Problems in many areas give rise to computationally expensive integrals that beg the need of efficient techniques to solve them, e.g., in computational finance for the modeling of cash flows; for the computation of Feynman loop integrals in high energy physics; and in stochastic geometry with applications to computer graphics. We demonstrate feasible numerical approaches […]
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Valentin Mena Morales, Pierre-Henri Horrein, Amer Baghdadi, Erik Hochapfel, Sandrine Vaton
Energy efficiency of financial computations is a performance criterion that can no longer be dismissed, and is as crucial as raw acceleration and accuracy of the solution. In order to reduce the energy consumption of financial accelerators, FPGAs offer a good compromise with low power consumption and high parallelism. However, designing and prototyping an application […]
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Ketsarin Rungraung, Putchong Uthayopas
The task of trading orders matching in financial markets is a very challenging task since due to the speed of arriving request. In this paper, the GPUs technology and CUDA programming is explored as a potential technology to accelerate this task. The trading method in Automatic Order Matching (AOM) of Stock Exchange of Thailand (SET) […]
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Michael Benguigui, Francoise Baude
This article presents a multi-GPU adaptation of a specific Monte Carlo and classification based method for pricing American basket options, due to Picazo. The first part relates how to combine fine and coarse-grained parallelization to price American basket options. A dynamic strategy of kernel calibration is proposed. Doing so, our implementation on a reasonable size […]
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Shih Hau Tan
Value-at-Risk (VaR) provides information about global risk in trading. The request for high speed calculation about VaR is rising because financial institutions need to measure the risk in real time. Researchers in HPC also recently turned their attention on this kind of demanding applications. In this master thesis, we introduce two complementary and different strategies […]
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Ying Peng, Hui Liu, Shuzhen Yang, Bin Gong
In this paper, we explore the opportunity for solving high dimensional Backward Stochastic Differential Equations (BSDEs) on the GPU with application in high dimensional American option pricing. A Least Square Monte Carlo method based numerical algorithm for solving the BSDEs is studied and summarized in four phases. For the parallel GPU algorithms of different phases, […]
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Matthew Anker
We have seen more and more interest in taking advantage of GPUs to accelerate simulations. However, the RNGs driving these simulations tend to be existing CPU generators that have been converted for use on GPUs. The result is a generator that does not efficiently utilise the resources and constraints of that architecture. Consequently, the performance […]
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Ryan Saunders
Computer modelling has been used for a number of years already to aid financial institutions in making business decisions. One such decision that financial firms are often faced with involves setting fair prices for financial options. Since the process of option pricing can be computationally expensive, methods of optimising it are sought after. One popular […]
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