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Using Particle Swarm Optimization Algorithm to Calibrate the Term Structure Model

Author

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  • Yanli Zhou
  • Shican Liu
  • Tianhai Tian
  • Qi He
  • Xiangyu Ge

Abstract

One of the advantages of stochastic differential equations (SDE) is that they can follow a variety of different trends so that they can establish complex dynamic systems in the economic and financial fields. Although some estimation methods have been proposed to identify the unknown parameters in virtue of the results in the SDE model to speed up the process, these solutions only focus on using explicit approach to solve SDEs, and therefore they are not reliable to deal with data source merged being large and varied. Thus, this study makes progress in creating a new implicit way to fill in the gaps of accurately calibrating the unknown parameters in the SDE model. Essentially, the primary goal of the article is to generate rigid SDE simulation. Meanwhile, the particle swarm optimization method serves a purpose to search and simultaneously obtain the optimal estimation of the model unknown parameters in the complicated experiment of parameter space in an effective way. Finally, in an interest rate term structure model, it is verified that the method effectively deals with parameter estimation in the SDE model.

Suggested Citation

  • Yanli Zhou & Shican Liu & Tianhai Tian & Qi He & Xiangyu Ge, 2021. "Using Particle Swarm Optimization Algorithm to Calibrate the Term Structure Model," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, January.
    • Handle: RePEc:hin:jnlmpe:8893940
    DOI: 10.1155/2021/8893940
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    Cited by:

    1. Couto, Luis. D. & Charkhgard, Mohammad & Karaman, Berke & Job, Nathalie & Kinnaert, Michel, 2023. "Lithium-ion battery design optimization based on a dimensionless reduced-order electrochemical model," Energy, Elsevier, vol. 263(PE).
    2. Bao, Daipengwei & Liu, Min & Ou, Yangwei & Xu, Qingshan & Li, Qin & Tan, Xiaoqing, 2024. "Eigenvalue-based quantum state verification of three-qubit W class states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
    3. Cui, Huixia & Chen, Xiangyong & Guo, Ming & Jiao, Yang & Cao, Jinde & Qiu, Jianlong, 2023. "A distribution center location optimization model based on minimizing operating costs under uncertain demand with logistics node capacity scalability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    4. Gerkani Nezhad Moshizi, Zahra & Bazrafshan, Ommolbanin & Ramezani Etedali, Hadi & Esmaeilpour, Yahya & Collins, Brain, 2023. "Application of inclusive multiple model for the prediction of saffron water footprint," Agricultural Water Management, Elsevier, vol. 277(C).

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