SSE/EFI Working Paper Series in Economics and Finance
No 652:
Modelling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH model
Annastiina Silvennoinen ()
and Timo Teräsvirta ()
Abstract: 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
Teräsvirta (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.
Keywords: Multivariate GARCH; Constant conditional correlation; Dynamic conditional correlation; Return comovement; Variable correlation GARCH model; Volatility model evaluation; (follow links to similar papers)
JEL-Codes: C12; C32; C51; C52; G10; (follow links to similar papers)
28 pages, February 1, 2007
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- This paper is published as:
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Silvennoinen, Annastiina and Timo Teräsvirta, (2009), 'Modeling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH model', Journal of Financial Econometrics, Vol. 7, pages 373-411
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