Consistency and asymptotic normality of maximum likelihood estimators of a multiplicative time-varying smooth transition correlation GARCH model

Publikation: Working paperForskning

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  • rp17_28

    Forlagets udgivne version, 839 KB, PDF-dokument

  • Annestiina Silvennoinen, Queensland University of Technology, Australien
  • Timo Terasvirta
A new multivariate volatility model that belongs to the family of conditional correlation GARCH models is introduced. The GARCH equations of this model contain a multiplicative deterministic component to describe long-run movements in volatility and, in addition, the correlations are deterministically time-varying. Parameters of the model are estimated jointly using maximum likelihood. Consistency and asymptotic normality of maximum likelihood estimators is proved. Numerical aspects of the estimation algorithm are discussed. A bivariate empirical example is provided.
OriginalsprogEngelsk
UdgivelsesstedAarhus
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider46
StatusUdgivet - 4 sep. 2017
SerietitelCREATES Research Papers
Nummer2017-28

    Forskningsområder

  • deterministically varying correlation, multiplicative time-varying GARCH, multivariate GARCH, nonstationary volatility, smooth transition GARCH

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