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 | |
| 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
Cite this
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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
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.
KW - Implicit stochastic method
KW - Particle swarm optimization
KW - Simulated maximum likelihood method
KW - Term structure of interest rates
UR - http://www.scopus.com/inward/record.url?scp=85050858419&partnerID=8YFLogxK
U2 - 10.1109/IIKI.2016.74
DO - 10.1109/IIKI.2016.74
M3 - Conference Paper
SP - 553
EP - 558
BT - Proceedings
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - Piscataway NJ USA
ER -