Augmented GARCH (p,q) process and its diffusion limit.

A family of parametric GARCH models, defined in terms of an auxiliary process and referred to as the augmented GARCH process is characterized and this process is shown to contain many existing parametric GARCH models. The augmented GARCH process can serve as a general alternative for Language Multi...

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Published in:Journal of Econometrics Vol. 79; pp. 97 - 128
Main Author: Duan, Jin-Chuan
Format: Article
Published: Elsevier Science July 1997
Subjects:
Online Access:View this record in EBSCOhost
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      dt: July 1997
      vid: 79
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      pub: Elsevier Science
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        513016996
        10.1016/S0304-4076(97)00009-2
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        atl: Augmented GARCH (p,q) process and its diffusion limit.
      aug:
        au: Duan, Jin-Chuan
      su:
        Time series analysis
        Mathematical transformations
        Stochastic processes
        Diffusion processes
      sug:
        subj:
          Time series analysis
          Mathematical transformations
          Stochastic processes
          Diffusion processes
      ab: A family of parametric GARCH models, defined in terms of an auxiliary process and referred to as the augmented GARCH process is characterized and this process is shown to contain many existing parametric GARCH models. The augmented GARCH process can serve as a general alternative for Language Multiplier test of many existing GARCH specifications. The diffusion limit of the augmented GARCH process is shown to contain many bivariate diffusion processes that are commonly used for modeling stochastic volatility in the finance literature. This convergence result generalizes that of Nelson (1990a) to cover a substantially larger class of GARCH(1,1) models and also extends to the GARCH(p,q) specification. The augmented GARCH process can be used as a direct approximation to the stochastic volatility models, or as the score generator in the efficient method of moments (Gallant and Tauchen, 1996) estimation of these models. Reprinted by permission of the publisher.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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