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An Extended Constant Conditional Correlation Garch Model And Its Fourth-Moment Structure

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  • He, Changli
  • Ter svirta, Timo
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    Abstract

    The constant conditional correlation general autoregressive conditional heteroskedasticity (GARCH) model is among the most commonly applied multivariate GARCH models and serves as a benchmark against which other models can be compared. In this paper we consider an extension to this model and examine its fourth-moment structure. The extension, first defined by Jeantheau (1998, Econometric Theory 14, 70 86), is motivated by the result found and discussed in this paper that the squared observations from the extended model have a rich autocorrelation structure. This means that already the first-order model is capable of reproducing a whole variety of autocorrelation structures observed in financial return series. These autocorrelations are derived for the first- and the second-order constant conditional correlation GARCH model. The usefulness of the theoretical results of the paper is demonstrated by reconsidering an empirical example that appeared in the original paper on the constant conditional correlation GARCH model.This research has been supported by the Swedish Research Council of Humanities and Social Sciences and the Tore Browaldh s Foundation. A part of this work was carried out while the second author was visiting the School of Finance and Economics, University of Technology, Sydney, whose kind hospitality is gratefully acknowledged. The paper has been presented at the Econometric Society European Meeting, Venice, August 2002. We thank participants for comments and two anonymous referees for their remarks. Any errors and shortcomings in the paper remain our own responsibility.

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    Bibliographic Info

    Article provided by Cambridge University Press in its journal Econometric Theory.

    Volume (Year): 20 (2004)
    Issue (Month): 05 (October)
    Pages: 904-926

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    Handle: RePEc:cup:etheor:v:20:y:2004:i:05:p:904-926_20

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    Cited by:
    1. Francq, Christian & Zakoian, Jean-Michel, 2010. "QML estimation of a class of multivariate GARCH models without moment conditions on the observed process," MPRA Paper 20779, University Library of Munich, Germany.
    2. Tomasz Wozniak, 2012. "Granger-causal analysis of VARMA-GARCH models," Economics Working Papers ECO2012/19, European University Institute.
    3. Conrad, Christian & Weber, Enzo, 2013. "Measuring Persistence in Volatility Spillovers," Working Papers 0543, University of Heidelberg, Department of Economics.
    4. Annastiina Silvennoinen & Timo Teräsvirta, 2008. "Multivariate GARCH models," CREATES Research Papers 2008-06, School of Economics and Management, University of Aarhus.
    5. Silvennoinen, Annastiina & Teräsvirta, Timo, 2005. "Multivariate Autoregressive Conditional Heteroskedasticity with Smooth Transitions in Conditional Correlations," Working Paper Series in Economics and Finance 577, Stockholm School of Economics, revised 01 Oct 2005.
    6. Chou, Ray Yeutien & Cai, Yijie, 2009. "Range-based multivariate volatility model with double smooth transition in conditional correlation," Global Finance Journal, Elsevier, vol. 20(2), pages 137-152.
    7. Conrad, Christian & Karanasos, Menelaos, 2010. "Negative Volatility Spillovers In The Unrestricted Eccc-Garch Model," Econometric Theory, Cambridge University Press, vol. 26(03), pages 838-862, June.
    8. Farhat Iqbal, 2013. "Robust estimation of the simplified multivariate GARCH model," Empirical Economics, Springer, vol. 44(3), pages 1353-1372, June.
    9. Tomasz Wozniak, 2012. "Testing Causality Between Two Vectors in Multivariate GARCH Models," Department of Economics - Working Papers Series 1139, The University of Melbourne.
    10. Zakoïan, Jean-Michel, 2010. "A class of DCC asymmetric GARCH models driven by exogenous variables," Economics Papers from University Paris Dauphine 123456789/5529, Paris Dauphine University.
    11. Alhaj-Yaseen, Yaseen S. & Lam, Eddery & Barkoulas, John T., 2014. "Price discovery for cross-listed firms with foreign IPOs," International Review of Financial Analysis, Elsevier, vol. 31(C), pages 80-87.
    12. Haas, Markus, 2010. "Covariance forecasts and long-run correlations in a Markov-switching model for dynamic correlations," Finance Research Letters, Elsevier, vol. 7(2), pages 86-97, June.
    13. Robert Garthoff & Iryna Okhrin & Wolfgang Schmid, 2014. "Statistical surveillance of the mean vector and the covariance matrix of nonlinear time series," AStA Advances in Statistical Analysis, Springer, vol. 98(3), pages 225-255, July.

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