Dissertation/Thesis
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Parameter Estimation and Model Testing for Markov Processes via Conditional Characteristic Functions

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Parameter Estimation and Model Testing for Markov Processes via Conditional Characteristic Functions
Συγγραφείς: Song Xi Chen A, Liang Peng C, Cindy L. Yu A
Συνεισφορές: The Pennsylvania State University CiteSeerX Archives
Πηγή: http://www.math.gatech.edu/~peng/publication/CCF.pdf.
Συλλογή: CiteSeerX
Θεματικοί όροι: Conditional characteristic function, Diffusion processes, Empirical likelihood, Kernel smoothing, Lévy driven processes 1
Περιγραφή: Markov processes are used in a wide range of disciplines including finance. The transitional densities of these processes are often unknown. However, the conditional characteristic functions are more likely to be available especially for Lévy driven processes. We propose an empirical likelihood approach for estimation and model specification test based on the conditional characteristic function for processes whose sample paths can be either continuous or discontinuous with jumps. An empirical likelihood estimator for the parameter of a parametric process, and a smoothed empirical likelihood ratio test for the parametric specification of the process are proposed, which are shown to have good theoretical properties and empirical performance. Simulations and empirical case study are carried out to confirm the effectiveness of the estimator and the test.
Τύπος εγγράφου: Thesis
Περιγραφή αρχείου: application/pdf
Γλώσσα: English
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.155.445; http://www.math.gatech.edu/~peng/publication/CCF.pdf
Διαθεσιμότητα: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.155.445
http://www.math.gatech.edu/~peng/publication/CCF.pdf
Rights: Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Αριθμός Καταχώρησης: edsbas.6CCC569F
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  Data: Parameter Estimation and Model Testing for Markov Processes via Conditional Characteristic Functions
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  Data: <searchLink fieldCode="AR" term="%22Song+Xi+Chen+A%22">Song Xi Chen A</searchLink><br /><searchLink fieldCode="AR" term="%22Liang+Peng+C%22">Liang Peng C</searchLink><br /><searchLink fieldCode="AR" term="%22Cindy+L%2E+Yu+A%22">Cindy L. Yu A</searchLink>
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  Data: The Pennsylvania State University CiteSeerX Archives
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  Data: Markov processes are used in a wide range of disciplines including finance. The transitional densities of these processes are often unknown. However, the conditional characteristic functions are more likely to be available especially for Lévy driven processes. We propose an empirical likelihood approach for estimation and model specification test based on the conditional characteristic function for processes whose sample paths can be either continuous or discontinuous with jumps. An empirical likelihood estimator for the parameter of a parametric process, and a smoothed empirical likelihood ratio test for the parametric specification of the process are proposed, which are shown to have good theoretical properties and empirical performance. Simulations and empirical case study are carried out to confirm the effectiveness of the estimator and the test.
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      – Text: English
    Subjects:
      – SubjectFull: Conditional characteristic function
        Type: general
      – SubjectFull: Diffusion processes
        Type: general
      – SubjectFull: Empirical likelihood
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      – SubjectFull: Kernel smoothing
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      – SubjectFull: Lévy driven processes 1
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