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Volatility impulse response functions for multivariate GARCH models


  • HAFNER, Christian

    () (Institut fur Statistik und Okonometrie, Wirtschaftswissenschaftliche Fakultat, Humboldt-Universitat zu Berlin, and CORE, Université catholique de Louvain)

  • HERWARTZ, Helmut

    () (Institut fur Statistik und Okonometrie, Wirtschaftswissenschaftliche Fakultat, Humboldt-Universitat zu Berlin)


In the empirical analysis of financial time series, multivariate GARCH models have been used in various forms.In most cases it is not well understood how the use of a restricted model has to be paid with loss of valuable information. We investigate the structural implications of two alternative models for the response of the conditional (co-)variances to independent shocks. The impulse response analysis, adopted to volatility models, appears to be a convenient methodology to obtain information on the interaction of financial series. We define volatility impulse response functions and provide an empirical analysis for a bivariate exchange rate series. For the analyzed series, the impulse response function of the correlation reveals strong discrepancies between the estimated diagonal and BEKK models. This indicates that the diagonality restriction may hide important structural properties of the series.

Suggested Citation

  • HAFNER, Christian & HERWARTZ, Helmut, 1998. "Volatility impulse response functions for multivariate GARCH models," CORE Discussion Papers 1998047, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Handle: RePEc:cor:louvco:1998047

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    References listed on IDEAS

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    Cited by:

    1. Hafner, Christian M., 2000. "Fourth moments of multivariate GARCH processes," SFB 373 Discussion Papers 2000,80, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Luis Fernando Melo Velandia & Oscar Reinaldo Becerra Camargo, 2006. "Una Aproximación a La Dinámica de las Tasas de Interés de Corto Plazo en Colombia a través de Modelos GARCH Multivariados," BORRADORES DE ECONOMIA 003694, BANCO DE LA REPÚBLICA.
    3. Walid Ben Omrane & Christian M. Hafner, 2009. "Information Spillover, Volatility and the Currency Markets for the Binary Choice Model," International Econometric Review (IER), Econometric Research Association, vol. 1(1), pages 50-62, April.
    4. Ólan T. Henry & Nilss Olekalns & Kalvinder K. Shields, 2013. "Quantifying time variation and asymmetry in measures of covariance risk: a simulation approach," Chapters,in: Handbook of Research Methods and Applications in Empirical Finance, chapter 18, pages 457-476 Edward Elgar Publishing.
    5. Kalvinder Shields & Nilss Olekalns & Ãlan T. Henry & Chris Brooks, 2005. "Measuring the Response of Macroeconomic Uncertainty to Shocks," The Review of Economics and Statistics, MIT Press, vol. 87(2), pages 362-370, May.
    6. Płuciennik Piotr, 2012. "Influence of the American Financial Market on Other Markets During the Subprime Crisis," Folia Oeconomica Stetinensia, De Gruyter Open, vol. 12(2), pages 19-30, December.
    7. Ekaterini Panopoulou & Theologos Pantelidis, 2009. "Integration at a cost: evidence from volatility impulse response functions," Applied Financial Economics, Taylor & Francis Journals, vol. 19(11), pages 917-933.
    8. Hafner, Christian M. & Rombouts, Jeroen V.K., 2007. "Semiparametric Multivariate Volatility Models," Econometric Theory, Cambridge University Press, vol. 23(02), pages 251-280, April.
    9. Olan T. Henry & Nilss Olekalns & Kalvinder Shields, 2004. "Time Variation And Asymmetry In The World Price Of Covariance Risk: The Implications For International Diversification," Department of Economics - Working Papers Series 907, The University of Melbourne.

    More about this item


    Multivariate GARCH; impulse response functions; exchange rate volatility.;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes


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