Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
Bauwens, L., Rombouts, J.: Bayesian clustering of many GARCH models. Econometric Reviews Special Issue Bayesian Dynamic Econometrics (2003)
Billio, M., Caporin, M., Gobbo, M.: Block Dynamic Conditional Correlation Multivariate GARCH models. Greta Working Paper (2003)
Billio, M., Caporin, M., Gobbo, M.: Flexible Dynamic Conditional Correlation multivariate GARCH models for asset allocation. Applied Financial Economics Letters 2, 123–130 (2006)
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)
Bollerslev, T.: Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics 31, 307–327 (1986)
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)
Ding, Z., Granger, C.W.J.: Modeling volatility persistence of speculative returns: A new approach. Journal of Econometrics 73, 185–215 (1996)
Engle, R.F., Sheppard, K.: Theoretical and Empirical properties of Dynamic Conditional Correlation Multivariate GARCH. Working paper, University of California, San Diego, CA (2001)
Engle, R.F.: Dynamic Conditional Correlation - a simple class of multivariate GARCH. Journal of Business and Economics Statistics 17, 425–446 (2002)
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)
Laurent, S., Bauwens, L., Jeroen, V.K.: Rombouts: Multivariate GARCH models: a survey. Journal of Applied Econometrics 21, 79–109 (2006)
Markowitz, H.: Portfolio Selection. Journal of Finance 7, 77–91 (1952)
Serban, M., Brockwell, A., Lehoczky, J., Srivastava, S.: Modeling the Dynamic Dependence Structure in Multivariate Financial Time Series. Journal of Time Series Analysis (2006)
Tse, Y.K., Tsui, A.K.C.: A note on diagnosing multivariate conditional heteroscedasticity models. Journal of Time Series Analysis 20, 679–691 (1999)
Vargas, G.A.: An Asymmetric Block Dynamic Conditional Correlation Multivariate GARCH Model. Philippines Statistician (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, T., Chan, L. (2008). Clustered Dynamic Conditional Correlation Multivariate GARCH Model. In: Song, IY., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2008. Lecture Notes in Computer Science, vol 5182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85836-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-540-85836-2_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85835-5
Online ISBN: 978-3-540-85836-2
eBook Packages: Computer ScienceComputer Science (R0)