Clustered Dynamic Conditional Correlation Multivariate GARCH Model

  • Tu Zhou
  • Laiwan Chan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5182)


The time-varying correlations between multivariate financial time series have been intensively studied. For example DCC and Block-DCC models have been proposed. In this paper, we present a novel Clustered DCC model which extends the previous models by incorporating clustering techniques. Instead of using the same parameters for all time series, a cluster structure is produced based on the autocorrelations of standardized residuals, in which clustered entries sharing the same dynamics. We compare and investigate different clustering methods using synthetic data. To verify the effectiveness of the whole proposed model, we conduct experiments on a set of Hong Kong stock daily returns, and the results outperform the original DCC GARCH model as well as Block-DCC model.


multivariate time series analysis GARCH DCC 


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  1. 1.
    Baillie, R.T., Chung, H.: Estimation of GARCH Models from the Autocorrelations of the Squares of a Process. Journal of Time Series Analysis 22, 631–650 (2001)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Bauwens, L., Rombouts, J.: Bayesian clustering of many GARCH models. Econometric Reviews Special Issue Bayesian Dynamic Econometrics (2003)Google Scholar
  3. 3.
    Billio, M., Caporin, M., Gobbo, M.: Block Dynamic Conditional Correlation Multivariate GARCH models. Greta Working Paper (2003)Google Scholar
  4. 4.
    Billio, M., Caporin, M., Gobbo, M.: Flexible Dynamic Conditional Correlation multivariate GARCH models for asset allocation. Applied Financial Economics Letters 2, 123–130 (2006)CrossRefGoogle Scholar
  5. 5.
    Billio, M., Caporin, M.: A Generalized Dynamic Conditional Correlation Model for Portfolio Risk Evaluation. Working Paper of the Department of Economics of the Ca’ Foscari University of Venice (2006)Google Scholar
  6. 6.
    Bollerslev, T.: Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics 31, 307–327 (1986)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Bollerslev, T.: Modelling the coherence in short-run nominal exchange rates: a multivareiate generalized ARCH approach. Review of Economic and Statistics 72, 498–505 (1999)CrossRefGoogle Scholar
  8. 8.
    Ding, Z., Granger, C.W.J.: Modeling volatility persistence of speculative returns: A new approach. Journal of Econometrics 73, 185–215 (1996)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Engle, R.F., Sheppard, K.: Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH. Working paper, University of California, San Diego, CA (2001)Google Scholar
  10. 10.
    Engle, R.F.: Dynamic Conditional Correlation - a simple class of multivariate GARCH. Journal of Business and Economics Statistics 17, 425–446 (2002)MathSciNetGoogle Scholar
  11. 11.
    Fa, J., Wang, M., Yao, Q.: Modeling Multivariate Volatilities via Conditionally Uncorrelated Components. Working Paper,Department of Statistics,London School of Economics and Political Science (2005)Google Scholar
  12. 12.
    Laurent, S., Bauwens, L., Jeroen, V.K.: Rombouts: Multivariate GARCH models: a survey. Journal of Applied Econometrics 21, 79–109 (2006)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Markowitz, H.: Portfolio Selection. Journal of Finance 7, 77–91 (1952)CrossRefGoogle Scholar
  14. 14.
    Serban, M., Brockwell, A., Lehoczky, J., Srivastava, S.: Modeling the Dynamic Dependence Structure in Multivariate Financial Time Series. Journal of Time Series Analysis (2006)Google Scholar
  15. 15.
    Tse, Y.K., Tsui, A.K.C.: A note on diagnosing multivariate conditional heteroscedasticity models. Journal of Time Series Analysis 20, 679–691 (1999)MATHCrossRefGoogle Scholar
  16. 16.
    Vargas, G.A.: An Asymmetric Block Dynamic Conditional Correlation Multivariate GARCH Model. Philippines Statistician (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Tu Zhou
    • 1
  • Laiwan Chan
    • 1
  1. 1.The Chinese University of Hong KongHong Kong 

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