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Bayesian Clustering Of Similar Multivariate Garch Models

Author

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  • Luc Bauwens
  • Jeroen Rombouts

Abstract

We consider the estimation of a large number of GARCH models, say of the order of several hundreds. Especially in the multivariate case, the number of parameters is extremely large. To reduce this number and render estimation feasible, we regroup the series in a small number of clusters. Within a cluster, the series share the same model and the same parameters. Each cluster should therefore contain similar series. What makes the problem interesting is that we do not know a piori which series belongs to which cluster. The overall model is therefore a finite mixture of distributions, where the weights of the components are unknown parameters and each component distribution has its own conditional mean and variance specification. Inference is done by the Bayesian approach, using data augmentation techniques. Illustrations are provided.

Suggested Citation

  • Luc Bauwens & Jeroen Rombouts, 2004. "Bayesian Clustering Of Similar Multivariate Garch Models," Econometric Society 2004 North American Winter Meetings 370, Econometric Society.
  • Handle: RePEc:ecm:nawm04:370
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    File URL: http://repec.org/esNAWM04/up.29383.1049138867.pdf
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    References listed on IDEAS

    as
    1. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    2. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
    3. Chib, Siddhartha & Hamilton, Barton H., 2000. "Bayesian analysis of cross-section and clustered data treatment models," Journal of Econometrics, Elsevier, vol. 97(1), pages 25-50, July.
    4. Kearney, Colm & Patton, Andrew J, 2000. "Multivariate GARCH Modeling of Exchange Rate Volatility Transmission in the European Monetary System," The Financial Review, Eastern Finance Association, vol. 35(1), pages 29-48, February.
    5. Bauwens, Luc & Lubrano, Michel & Richard, Jean-Francois, 2000. "Bayesian Inference in Dynamic Econometric Models," OUP Catalogue, Oxford University Press, number 9780198773139.
    6. 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 351-362, July.
    7. repec:cup:etheor:v:11:y:1995:i:1:p:122-50 is not listed on IDEAS
    8. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
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    More about this item

    Keywords

    Large financial systems; Multivariate GARCH; Clustering; Bayesian methods; Gibbs sampling; Finite mixture distributions;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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