Continuous-time Identification of Exponential-Affine Term Structure Models

A.W. Arianto Wibowo

    Research output: ThesisPhD Thesis - Research UT, graduation UT

    28 Downloads (Pure)

    Abstract

    This thesis addresses the problem of parameter estimation of the exponentialaffine class of models, which is a class of multi-factor models for the short rate. We propose a continuous-time maximum likelihood estimation method to estimate the parameters of a short rate model, given set of observations that are linear with respect to the interest rate factors. We assume that observations are corrupted by Gaussian noise with a known covariance, which lead to a maximum likelihood estimation method for partially observed systems. Unlike other approaches in the literature, we do not discretize either the interest rate model or the observation model.
    Original languageUndefined
    Awarding Institution
    • University of Twente
    Supervisors/Advisors
    • Bagchi, Arunabha, Supervisor
    Award date6 Dec 2006
    Place of PublicationEnschede
    Publisher
    Print ISBNs90-365-2442-3
    Publication statusPublished - 6 Dec 2006

    Keywords

    • METIS-237720
    • IR-57641
    • EWI-8429

    Cite this

    Arianto Wibowo, A. W. (2006). Continuous-time Identification of Exponential-Affine Term Structure Models. Enschede: IEEE Computer Society Press.
    Arianto Wibowo, A.W.. / Continuous-time Identification of Exponential-Affine Term Structure Models. Enschede : IEEE Computer Society Press, 2006. 71 p.
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    title = "Continuous-time Identification of Exponential-Affine Term Structure Models",
    abstract = "This thesis addresses the problem of parameter estimation of the exponentialaffine class of models, which is a class of multi-factor models for the short rate. We propose a continuous-time maximum likelihood estimation method to estimate the parameters of a short rate model, given set of observations that are linear with respect to the interest rate factors. We assume that observations are corrupted by Gaussian noise with a known covariance, which lead to a maximum likelihood estimation method for partially observed systems. Unlike other approaches in the literature, we do not discretize either the interest rate model or the observation model.",
    keywords = "METIS-237720, IR-57641, EWI-8429",
    author = "{Arianto Wibowo}, A.W.",
    year = "2006",
    month = "12",
    day = "6",
    language = "Undefined",
    isbn = "90-365-2442-3",
    publisher = "IEEE Computer Society Press",
    school = "University of Twente",

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    Arianto Wibowo, AW 2006, 'Continuous-time Identification of Exponential-Affine Term Structure Models', University of Twente, Enschede.

    Continuous-time Identification of Exponential-Affine Term Structure Models. / Arianto Wibowo, A.W.

    Enschede : IEEE Computer Society Press, 2006. 71 p.

    Research output: ThesisPhD Thesis - Research UT, graduation UT

    TY - THES

    T1 - Continuous-time Identification of Exponential-Affine Term Structure Models

    AU - Arianto Wibowo, A.W.

    PY - 2006/12/6

    Y1 - 2006/12/6

    N2 - This thesis addresses the problem of parameter estimation of the exponentialaffine class of models, which is a class of multi-factor models for the short rate. We propose a continuous-time maximum likelihood estimation method to estimate the parameters of a short rate model, given set of observations that are linear with respect to the interest rate factors. We assume that observations are corrupted by Gaussian noise with a known covariance, which lead to a maximum likelihood estimation method for partially observed systems. Unlike other approaches in the literature, we do not discretize either the interest rate model or the observation model.

    AB - This thesis addresses the problem of parameter estimation of the exponentialaffine class of models, which is a class of multi-factor models for the short rate. We propose a continuous-time maximum likelihood estimation method to estimate the parameters of a short rate model, given set of observations that are linear with respect to the interest rate factors. We assume that observations are corrupted by Gaussian noise with a known covariance, which lead to a maximum likelihood estimation method for partially observed systems. Unlike other approaches in the literature, we do not discretize either the interest rate model or the observation model.

    KW - METIS-237720

    KW - IR-57641

    KW - EWI-8429

    M3 - PhD Thesis - Research UT, graduation UT

    SN - 90-365-2442-3

    PB - IEEE Computer Society Press

    CY - Enschede

    ER -

    Arianto Wibowo AW. Continuous-time Identification of Exponential-Affine Term Structure Models. Enschede: IEEE Computer Society Press, 2006. 71 p.