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Efficient estimation of a multivariate multiplicative volatility model

  • Hafner, Christian M.
  • Linton, Oliver

We propose a multivariate generalization of the multiplicative volatility model of Engle and Rangel (2008), which has a nonparametric long run component and a unit multivariate GARCH short run dynamic component. We suggest various kernel-based estimation procedures for the parametric and nonparametric components, and derive the asymptotic properties thereof. For the parametric part of the model, we obtain the semiparametric efficiency bound. Our method is applied to a bivariate stock index series. We find that the univariate model of Engle and Rangel (2008) appears to be violated in the data whereas our multivariate model is more consistent with the data.

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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 159 (2010)
Issue (Month): 1 (November)
Pages: 55-73

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Handle: RePEc:eee:econom:v:159:y:2010:i:1:p:55-73
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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  1. Roy van der Weide, 2004. "Wake me up before you GO-GARCH," Computing in Economics and Finance 2004 316, Society for Computational Economics.
  2. Lanne, Markku & Saikkonen, Pentti, 2005. "A Multivariate Generalized Orthogonal Factor GARCH Model," MPRA Paper 23714, University Library of Munich, Germany.
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  16. Jose Gonzalo Rangel & Robert F. Engle, 2009. "The Factor-Spline-GARCH Model for High and Low Frequency Correlations," Working Papers 2009-03, Banco de México.
  17. Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-62, July.
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  19. Robert F. Engle & Kevin Sheppard, 2001. "Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH," NBER Working Papers 8554, National Bureau of Economic Research, Inc.
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