Inference of Term Structure Models

Yanli Zhou; Xiangyu Ge; Yonghong Wu; Tianhai Tian

doi:10.1109/IIKI.2016.74

Inference of Term Structure Models

Yanli Zhou, Xiangyu Ge, Yonghong Wu, Tianhai Tian

School of Mathematics

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

1 Citation (Scopus)

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 language	English
Title of host publication	Proceedings
Subtitle of host publication	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
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	553-558
Number of pages	6
ISBN (Electronic)	9781509059522
DOIs	https://doi.org/10.1109/IIKI.2016.74
Publication status	Published - 2017
Event	International Conference on Identification, Information and Knowledge in the Internet of Things, 2016 - Beijing, China Duration: 20 Oct 2016 → 21 Oct 2016

Conference

Conference	International Conference on Identification, Information and Knowledge in the Internet of Things, 2016
Abbreviated title	IIKI 2016
Country	China
City	Beijing
Period	20/10/16 → 21/10/16

Keywords

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

Access to Document

10.1109/IIKI.2016.74

Link to publication in Scopus

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). 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.",

keywords = "Implicit stochastic method, Particle swarm optimization, Simulated maximum likelihood method, Term structure of interest rates",

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language = "English",

pages = "553--558",

booktitle = "Proceedings",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

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note = "International Conference on Identification, Information and Knowledge in the Internet of Things, 2016, IIKI 2016 ; Conference date: 20-10-2016 Through 21-10-2016",

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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 proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Inference of Term Structure Models

AU - Zhou, Yanli

AU - Ge, Xiangyu

AU - Wu, Yonghong

AU - Tian, Tianhai

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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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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