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Method of moments estimation of GO-GARCH models

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  • Peter Boswijk, H.
  • van der Weide, Roy
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    Abstract

    We propose a new estimation method for the factor loading matrix in generalized orthogonal GARCH (GO-GARCH) models. The method is based on eigenvectors of suitably defined sample autocorrelation matrices of squares and cross-products of returns. The method is numerically more attractive than likelihood-based estimation. Furthermore, the new method does not require strict assumptions on the volatility models of the factors, and therefore is less sensitive to model misspecification. We provide conditions for consistency of the estimator, and study its efficiency relative to maximum likelihood estimation using Monte Carlo simulations. The method is applied to European sector returns.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0304407610002150
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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Econometrics.

    Volume (Year): 163 (2011)
    Issue (Month): 1 (July)
    Pages: 118-126

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    Handle: RePEc:eee:econom:v:163:y:2011:i:1:p:118-126

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    Web page: http://www.elsevier.com/locate/jeconom

    Related research

    Keywords: Multivariate GARCH Factor models Method of moments Common principal components;

    References

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    1. Ledoit, Olivier & Wolf, Michael, 2004. "A well-conditioned estimator for large-dimensional covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 365-411, February.
    2. Comte, F. & Lieberman, O., 2003. "Asymptotic theory for multivariate GARCH processes," Journal of Multivariate Analysis, Elsevier, vol. 84(1), pages 61-84, January.
    3. H. Peter Boswijk & Roy van der Weide, 2006. "Wake me up before you GO-GARCH," Tinbergen Institute Discussion Papers 06-079/4, Tinbergen Institute, revised 21 Sep 2006.
    4. He, Changli & Terasvirta, Timo, 1999. "Properties of moments of a family of GARCH processes," Journal of Econometrics, Elsevier, vol. 92(1), pages 173-192, September.
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    8. Hafner, C.M., 2004. "Temporal aggregation of multivariate GARCH processes," Econometric Institute Research Papers EI 2004-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. Boswijk, H.P. & Weide, R. van der, 2006. "Wake me up before you GO-GARCH," CeNDEF Working Papers 06-13, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
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    14. Sentana, Enrique & Fiorentini, Gabriele, 2001. "Identification, estimation and testing of conditionally heteroskedastic factor models," Journal of Econometrics, Elsevier, vol. 102(2), pages 143-164, June.
    15. Catherine Doz & Eric Renault, 2006. "Factor Stochastic Volatility in Mean Models: A GMM Approach," Econometric Reviews, Taylor & Francis Journals, vol. 25(2-3), pages 275-309.
    16. Christian M. Hafner, 2003. "Fourth Moment Structure of Multivariate GARCH Models," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 1(1), pages 26-54.
    17. François Longin, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, 04.
    18. Lanne, Markku & Saikkonen, Pentti, 2005. "A Multivariate Generalized Orthogonal Factor GARCH Model," MPRA Paper 23714, University Library of Munich, Germany.
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    Cited by:
    1. Diaa Noureldin & Neil Shephard & Kevin Sheppard, 2012. "Multivariate Rotated ARCH Models," Economics Papers 2012-W01, Economics Group, Nuffield College, University of Oxford.
    2. Noureldin, Diaa & Shephard, Neil & Sheppard, Kevin, 2014. "Multivariate rotated ARCH models," Journal of Econometrics, Elsevier, vol. 179(1), pages 16-30.

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