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Authors = Timo Teräsvirta ORCID = 0000-0002-0947-2114

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37 pages, 1354 KiB  
Article
Building Multivariate Time-Varying Smooth Transition Correlation GARCH Models, with an Application to the Four Largest Australian Banks
by Anthony D. Hall, Annastiina Silvennoinen and Timo Teräsvirta
Econometrics 2023, 11(1), 5; https://doi.org/10.3390/econometrics11010005 - 6 Feb 2023
Cited by 3 | Viewed by 3581
Abstract
This paper proposes a methodology for building Multivariate Time-Varying STCC–GARCH models. The novel contributions in this area are the specification tests related to the correlation component, the extension of the general model to allow for additional correlation regimes, and a detailed exposition of [...] Read more.
This paper proposes a methodology for building Multivariate Time-Varying STCC–GARCH models. The novel contributions in this area are the specification tests related to the correlation component, the extension of the general model to allow for additional correlation regimes, and a detailed exposition of the systematic, improved modelling cycle required for such nonlinear models. There is an R-package that includes the steps in the modelling cycle. Simulations demonstrate the robustness of the recommended model building approach. The modelling cycle is illustrated using daily return series for Australia’s four largest banks. Full article
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41 pages, 2020 KiB  
Article
A Parsimonious Test of Constancy of a Positive Definite Correlation Matrix in a Multivariate Time-Varying GARCH Model
by Jian Kang, Johan Stax Jakobsen, Annastiina Silvennoinen, Timo Teräsvirta and Glen Wade
Econometrics 2022, 10(3), 30; https://doi.org/10.3390/econometrics10030030 - 24 Aug 2022
Cited by 1 | Viewed by 3125
Abstract
We construct a parsimonious test of constancy of the correlation matrix in the multivariate conditional correlation GARCH model, where the GARCH equations are time-varying. The alternative to constancy is that the correlations change deterministically as a function of time. The alternative is a [...] Read more.
We construct a parsimonious test of constancy of the correlation matrix in the multivariate conditional correlation GARCH model, where the GARCH equations are time-varying. The alternative to constancy is that the correlations change deterministically as a function of time. The alternative is a covariance matrix, not a correlation matrix, so the test may be viewed as a general test of stability of a constant correlation matrix. The size of the test in finite samples is studied by simulation. An empirical example involving daily returns of 26 stocks included in the Dow Jones stock index is given. Full article
(This article belongs to the Special Issue Special Issue on Time Series Econometrics)
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