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  4. Vol 5, Issue 4, 2022

High-Performance Computing Option Pricing Algorithm on Hybrid Heterogeneous Many-Core Platforms

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DOI: 10.23977/jaip.2022.050405 | Downloads: 8 | Views: 477

Author(s)

Ling Yang 1, Yijia Zhang 1, Lei Wang 1, Yu Guan 1

Affiliation(s)

1 School of Informatics, Harbin Guangsha College, Harbin, Heilongjiang, 150025, China

Corresponding Author

Ling Yang

ABSTRACT

In recent years, financial derivative securities have developed rapidly, and the pricing of contingent rights has also attracted widespread attention from domestic and foreign scholars. Option pricing theory has become an important theory that won the Nobel Prize in Economics after asset portfolio theory and capital asset pricing model. This paper aims to study the pricing algorithm of high-performance computing options on hybrid heterogeneous many-core platforms. In this paper, particle swarm optimization algorithm (PSO), quantum behavioral particle swarm algorithm (QPSO), differential evolution algorithm (DE), and evolution strategy (ES) are used to solve the parameter estimation of nonlinear option pricing models, using the nonlinear approximation of the algorithm ability to establish an algorithm model for solving parameter estimates, and use the weighted sum of the squared errors of the experimental value and the predicted value of the algorithm as the objective optimization function. Find the most suitable optimization algorithm to solve this problem through experiments. In addition, the Internet of Things technology is also used to design the Internet of Things data collection system, and the RFID technology, sensor technology, wireless network technology, artificial intelligence technology, and cloud computing technology in the Internet of Things are analyzed. The results used in this paper show that the parameters of option pricing are estimated by four algorithms. Among them, the QPSO algorithm has the best convergence performance and the algorithm fitting results are 1.7 times more accurate than the worst algorithm, followed by the DE algorithm, PSO, and ES The effect of algorithm parameter fitting is poor.

KEYWORDS

Internet of Things, Smart Environment, Hybrid Heterogeneous Many-Core Platform, High-Performance Computing, Option Pricing Algorithm

CITE THIS PAPER

Ling Yang, Yijia Zhang, Lei Wang, Yu Guan, High-Performance Computing Option Pricing Algorithm on Hybrid Heterogeneous Many-Core Platforms. Journal of Artificial Intelligence Practice (2022) Vol. 5: 27-39. DOI: http://dx.doi.org/10.23977/jaip.2022.050405.

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