Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-28T13:16:45.708Z Has data issue: false hasContentIssue false

AN EXTENDED CONSTANT CONDITIONAL CORRELATION GARCH MODEL AND ITS FOURTH-MOMENT STRUCTURE

Published online by Cambridge University Press:  01 October 2004

Changli He
Affiliation:
Stockholm School of Economics
Timo Teräsvirta
Affiliation:
Stockholm School of Economics

Abstract

The constant conditional correlation general autoregressive conditional heteroskedasticity (GARCH) model is among the most commonly applied multivariate GARCH models and serves as a benchmark against which other models can be compared. In this paper we consider an extension to this model and examine its fourth-moment structure. The extension, first defined by Jeantheau (1998, Econometric Theory 14, 70–86), is motivated by the result found and discussed in this paper that the squared observations from the extended model have a rich autocorrelation structure. This means that already the first-order model is capable of reproducing a whole variety of autocorrelation structures observed in financial return series. These autocorrelations are derived for the first- and the second-order constant conditional correlation GARCH model. The usefulness of the theoretical results of the paper is demonstrated by reconsidering an empirical example that appeared in the original paper on the constant conditional correlation GARCH model.This research has been supported by the Swedish Research Council of Humanities and Social Sciences and the Tore Browaldh's Foundation. A part of this work was carried out while the second author was visiting the School of Finance and Economics, University of Technology, Sydney, whose kind hospitality is gratefully acknowledged. The paper has been presented at the Econometric Society European Meeting, Venice, August 2002. We thank participants for comments and two anonymous referees for their remarks. Any errors and shortcomings in the paper remain our own responsibility.

Type
Research Article
Copyright
© 2004 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Bollerslev, T. (1986) Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31, 307327.Google Scholar
Bollerslev, T. (1990) Modelling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model. Review of Economics and Statistics 72, 498505.Google Scholar
Bollerslev, T. & R.F. Engle (1993) Common persistence in conditional variances. Econometrica 61, 167186.Google Scholar
Bollerslev, T., R.F. Engle, & D.B. Nelson (1994) ARCH models. In R.F. Engle & D.L. McFadden (eds.), Handbook of Econometrics, vol. 4, pp. 29593038. Elsevier.
Carrasco, M. & X. Chen (2002) Mixing and moment properties of various GARCH and stochastic volatility models. Econometric Theory 18, 1739.Google Scholar
Cecchetti, S.G., R.E. Cumby, & S. Figlewski (1988) Estimation of the optimal futures hedge. Review of Economics and Statistics 70, 623630.Google Scholar
Engle, R.F. (2002) Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroscedasticity models. Journal of Business and Economic Statistics 20, 339350.Google Scholar
Engle, R.F. & K.F. Kroner (1995) Multivariate simultaneous generalized ARCH. Econometric Theory 11, 122150.Google Scholar
Gouriéroux, C. (1997) ARCH Models and Financial Applications. Springer.
Hafner, C.M. (2003) Fourth moment structure of multivariate GARCH models. Journal of Financial Econometrics 1, 2654.Google Scholar
He, C. & T. Teräsvirta (1999a) Fourth moment structure of the GARCH(p,q) process. Econometric Theory 15, 824846.Google Scholar
He, C. & T. Teräsvirta (1999b) Properties of moments of a family of GARCH processes. Journal of Econometrics 92, 173192.Google Scholar
Jeantheau, T. (1998) Strong consistency of estimations of multivariate ARCH model. Econometric Theory 14, 7086.Google Scholar
Karanasos, M. (1999) The second moment and the autocovariance function of the squared errors of the GARCH model. Journal of Econometrics 90, 6376.Google Scholar
Karanasos, M. (2003) Some Exact Formulae for the Constant Correlation and Diagonal M-GARCH Models. Paper, Department of Economics, University of York.
Lanne, M. & P. Saikkonen (2002) Nonlinear GARCH Models for Highly Persistent Volatility. Working paper, University of Helsinki.
Ling, S. & M. McAleer (2002) Necessary and sufficient moment conditions for the GARCH(r,s) and asymmetric power GARCH(r,s) models. Econometric Theory 18, 722729.Google Scholar
Ling, S. & M. McAleer (2003) Asymptotic theory for a vector ARMA-GARCH model. Econometric Theory 19, 280310.Google Scholar
Magnus, J.R. (1988) Linear Structures. Griffin.
Nelson, D.B. & C.Q. Cao (1992) Inequality constraints in the univariate GARCH model. Journal of Business and Economic Statistics 10, 229235.Google Scholar
Palm, F.C. (1996) GARCH models of volatility. In G.S. Maddala & C.R. Rao (eds.), Handbook of Statistics, vol. 14, pp. 209240. Elsevier.
Tong, H. (1990) Non-Linear Time Series: A Dynamical System Approach. Oxford University Press.
Tse, Y. & A. Tsui (2002) A multivariate GARCH model with time-varying correlations. Journal of Business and Economic Statistics 20, 351362.Google Scholar
Wong, H., W.K. Li, & S. Ling (2002) A Cointegrated Conditional Heteroscedastic Model with Applications. Working paper, Department of Applied Mathematics, Hong Kong Polytechnic University.