Inference of Term Structure Models

Yanli Zhou, Xiangyu Ge, Yonghong Wu, Tianhai Tian

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Compared with deterministic models, the key feature of a stochastic differential equation (SDE) model is its ability to generate a large number of different trajectories. To tackle the challenge, a number of methods have been proposed to infer reliable estimates. But these methods dominantly used the explicit methods for solving SDEs, and thus are not appropriate to deal with experimentaldata with large variations. In this work we develop a new method by using implicit methods to solve SDEs, which is aimed at generating stable simulations for stiff SDE models. The particle swarm optimization method is used as an efficient searching method to explore the optimal estimate in the complex parameter space. Using the interest term structure model as the test system, numerical results showed that the proposed new method is an effective approach for generating reliable estimates of unknown parameters in SDE models.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publication2016 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2016; Beijing; China; 20 October 2016 through 21 October 2016; Category number E6070
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages553-558
Number of pages6
ISBN (Electronic)9781509059522
DOIs
Publication statusPublished - 2017
EventInternational Conference on Identification, Information and Knowledge in the Internet of Things, 2016 - Beijing, China
Duration: 20 Oct 201621 Oct 2016

Conference

ConferenceInternational Conference on Identification, Information and Knowledge in the Internet of Things, 2016
Abbreviated titleIIKI 2016
CountryChina
CityBeijing
Period20/10/1621/10/16

Keywords

  • Implicit stochastic method
  • Particle swarm optimization
  • Simulated maximum likelihood method
  • Term structure of interest rates

Cite this

Zhou, Y., Ge, X., Wu, Y., & Tian, T. (2017). Inference of Term Structure Models. In Proceedings: 2016 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2016; Beijing; China; 20 October 2016 through 21 October 2016; Category number E6070 (pp. 553-558). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IIKI.2016.74
Zhou, Yanli ; Ge, Xiangyu ; Wu, Yonghong ; Tian, Tianhai. / Inference of Term Structure Models. Proceedings: 2016 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2016; Beijing; China; 20 October 2016 through 21 October 2016; Category number E6070. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 553-558
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title = "Inference of Term Structure Models",
abstract = "Compared with deterministic models, the key feature of a stochastic differential equation (SDE) model is its ability to generate a large number of different trajectories. To tackle the challenge, a number of methods have been proposed to infer reliable estimates. But these methods dominantly used the explicit methods for solving SDEs, and thus are not appropriate to deal with experimentaldata with large variations. In this work we develop a new method by using implicit methods to solve SDEs, which is aimed at generating stable simulations for stiff SDE models. The particle swarm optimization method is used as an efficient searching method to explore the optimal estimate in the complex parameter space. Using the interest term structure model as the test system, numerical results showed that the proposed new method is an effective approach for generating reliable estimates of unknown parameters in SDE models.",
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Zhou, Y, Ge, X, Wu, Y & Tian, T 2017, Inference of Term Structure Models. in Proceedings: 2016 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2016; Beijing; China; 20 October 2016 through 21 October 2016; Category number E6070. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 553-558, International Conference on Identification, Information and Knowledge in the Internet of Things, 2016, Beijing, China, 20/10/16. https://doi.org/10.1109/IIKI.2016.74

Inference of Term Structure Models. / Zhou, Yanli; Ge, Xiangyu; Wu, Yonghong; Tian, Tianhai.

Proceedings: 2016 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2016; Beijing; China; 20 October 2016 through 21 October 2016; Category number E6070. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 553-558.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

TY - GEN

T1 - Inference of Term Structure Models

AU - Zhou, Yanli

AU - Ge, Xiangyu

AU - Wu, Yonghong

AU - Tian, Tianhai

PY - 2017

Y1 - 2017

N2 - Compared with deterministic models, the key feature of a stochastic differential equation (SDE) model is its ability to generate a large number of different trajectories. To tackle the challenge, a number of methods have been proposed to infer reliable estimates. But these methods dominantly used the explicit methods for solving SDEs, and thus are not appropriate to deal with experimentaldata with large variations. In this work we develop a new method by using implicit methods to solve SDEs, which is aimed at generating stable simulations for stiff SDE models. The particle swarm optimization method is used as an efficient searching method to explore the optimal estimate in the complex parameter space. Using the interest term structure model as the test system, numerical results showed that the proposed new method is an effective approach for generating reliable estimates of unknown parameters in SDE models.

AB - Compared with deterministic models, the key feature of a stochastic differential equation (SDE) model is its ability to generate a large number of different trajectories. To tackle the challenge, a number of methods have been proposed to infer reliable estimates. But these methods dominantly used the explicit methods for solving SDEs, and thus are not appropriate to deal with experimentaldata with large variations. In this work we develop a new method by using implicit methods to solve SDEs, which is aimed at generating stable simulations for stiff SDE models. The particle swarm optimization method is used as an efficient searching method to explore the optimal estimate in the complex parameter space. Using the interest term structure model as the test system, numerical results showed that the proposed new method is an effective approach for generating reliable estimates of unknown parameters in SDE models.

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KW - Particle swarm optimization

KW - Simulated maximum likelihood method

KW - Term structure of interest rates

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Zhou Y, Ge X, Wu Y, Tian T. Inference of Term Structure Models. In Proceedings: 2016 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2016; Beijing; China; 20 October 2016 through 21 October 2016; Category number E6070. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 553-558 https://doi.org/10.1109/IIKI.2016.74