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A component GARCH model with time varying weights

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  • Giuseppe Storti

    ()
    (Department of Economics and Statistics University of Salerno)

  • Luc Bauwens

    (CORE, Université catholique de Louvain, Belgium)

Abstract

The empirical evidence from financial markets suggests that the pattern of response of market volatility to shocks is highly dependent on the magnitude of shocks themselves. Markov-Switching GARCH (MS-GARCH) models are a valuable tool for modelling state dependence in the dynamics of the volatility process. However, their application is still limited by the severe difficulties arising at the estimation and identification stages. In order to allow for time varying persistence in the volatility dynamics, it is here suggested to use a modification of the component GARCH model proposed by Ding and Granger (1996) in which the weights associated to the model components are time varying and depend on adequately chosen state variables such as lagged values of the conditional standard deviation. Differently from MS-GARCH models, likelihood based inference for the proposed model is readily available using standard numerical tools. Since the proposed model implies a non-linear representation for the squared observations, the generation of multi-step-ahead volatility predictions imposes some additional difficulties with respect to standard GARCH models, for which a linear ARMA representation can be obtained. In the paper, we apply simulation based techniques for estimating the predictive density of returns.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 388.

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Date of creation: 04 Jul 2006
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Handle: RePEc:sce:scecfa:388

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Keywords: GARCH; persistence; volatility components; Value at Risk;

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References

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  1. Baillie, Richard T. & Bollerslev, Tim, 1992. "Prediction in dynamic models with time-dependent conditional variances," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 91-113.
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Citations

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Cited by:
  1. Haas, Markus & Mittnik, Stefan & Paolella, Marc S., 2009. "Asymmetric multivariate normal mixture GARCH," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2129-2154, April.
  2. Luc Bauwens & Christian M. Hafner & Diane Pierret, 2011. "Multivariate Volatility Modeling of Electricity Futures," SFB 649 Discussion Papers SFB649DP2011-063, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  3. BAUWENS, Luc & HAFNER, Christian & LAURENT, Sébastien, 2011. "Volatility models," CORE Discussion Papers 2011058, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  4. Bouoiyour, Jamal & Selmi, Refk, 2013. "The controversial link between exchange rate volatility and exports: Evidence from Tunisian case," MPRA Paper 49133, University Library of Munich, Germany, revised Mar 2013.
  5. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It’s all about volatility (of volatility): evidence from a two-factor stochastic volatility model," CREATES Research Papers 2013-03, School of Economics and Management, University of Aarhus.
  6. Boudt, Kris & Daníelsson, Jón & Laurent, Sébastien, 2013. "Robust forecasting of dynamic conditional correlation GARCH models," International Journal of Forecasting, Elsevier, vol. 29(2), pages 244-257.

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