JOURNAL ARTICLE
Research on the Construction of Financial Computing Model Based on BSDE Algorithm.
Published In: Journal of Information & Knowledge Management, 2023, v. 22, n. 4. P. 1 1 of 3
Database: The Belt and Road Initiative Reference Source 2 of 3
Authored By: Cai, Youli 3 of 3
Abstract
Economic and social development has made financial engineering an increasingly important research area, and more and more financial problems cannot be solved directly by analytical formulas. In view of this, algorithms that apply computer technology to financial engineering have emerged. In this study, the Backward Stochastic Differential Equation (BSDE) algorithm is used to investigate and analyse the problem of option pricing calculation in finance. In the research process, GBSDE-Theta parallel algorithm composed of BSDE-Theta algorithm and GPU algorithm uses the new algorithm to establish a computing model in the financial engineering field, which applies to the calculation of enterprise option pricing. The research results show that compared with the basic algorithm, the actual option values of the option pricing data obtained by using the GBSDE-Theta parallel algorithm are more closely matched. The computational model can achieve a speedup ratio of about 230 times of the serial version with the number of time steps N = 1 2 8 and the number of simulated paths 80,000. About the relative error of the GBSDE-Theta algorithm, there are 80 points within 3% and only 16 points over 3.00%, which is a relatively small error. The above results show that the financial computing system obtained in this study is highly feasible and effective, and can provide a new research idea for the progress and development of other computations in the financial field. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Information & Knowledge Management. 2023/08, Vol. 22, Issue 4, p1
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2023
- ISSN:0219-6492
- DOI:10.1142/S0219649223500296
- Accession Number:171368939
- Copyright Statement:Copyright of Journal of Information & Knowledge Management is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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