Department of Economics and Business Economics

Timo Teräsvirta

Modelling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH Model

Research output: Working paperResearch

    Annastiina Silvennoinen, University of Technology Sydney, Australia
  • Timo Teräsvirta
  • School of Economics and Management
In this paper we propose a multivariate GARCH model with a time-varying conditional
correlation structure. The new Double Smooth Transition Conditional Correlation GARCH
model extends the Smooth Transition Conditional Correlation GARCH model of Silvennoinen
and Ter¨asvirta (2005) by including another variable according to which the correlations
change smoothly between states of constant correlations. A Lagrange multiplier test
is derived to test the constancy of correlations against the DSTCC-GARCH model, and
another one to test for another transition in the STCC-GARCH framework. In addition,
other specification tests, with the aim of aiding the model building procedure, are considered.
Analytical expressions for the test statistics and the required derivatives are provided.
The model is applied to a selection of world stock indices, and it is found that time is an
important factor affecting correlations between them.
Original languageEnglish
Place of publicationAarhus
PublisherInstitut for Økonomi, Aarhus Universitet
Number of pages32
StatePublished - 2008

    Research areas

  • Multivariate GARCH; Constant conditional correlation; Dynamic conditional

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