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

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Author Info

  • Silvennoinen, Annastiina

    ()
    (School of Finance and Economics)

  • Teräsvirta, Timo

    ()
    (CREATES)

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.

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Bibliographic Info

Paper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 0652.

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Length: 28 pages
Date of creation: 01 Feb 2007
Date of revision:
Publication status: Published in Journal of Financial Econometrics, 2009, pages 373-411.
Handle: RePEc:hhs:hastef:0652

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Postal: The Economic Research Institute, Stockholm School of Economics, P.O. Box 6501, 113 83 Stockholm, Sweden
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Web page: http://www.hhs.se/
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Keywords: Multivariate GARCH; Constant conditional correlation; Dynamic conditional correlation; Return comovement; Variable correlation GARCH model; Volatility model evaluation;

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