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    Inference of Term Structure Models

    Access Status
    Fulltext not available
    Authors
    Zhou, Y.
    Ge, X.
    Wu, Yong Hong
    Tian, T.
    Date
    2018
    Type
    Conference Paper
    
    Metadata
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    Citation
    Zhou, Y. and Ge, X. and Wu, Y.H. and Tian, T. 2018. Inference of Term Structure Models, pp. 553-558.
    Source Title
    Proceedings - 2016 International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2016
    DOI
    10.1109/IIKI.2016.74
    ISBN
    9781509059522
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/71184
    Collection
    • Curtin Research Publications
    Abstract

    © 2016 IEEE. 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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