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A Multivariate GARCH Model with Time-Varying Correlations

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  • Y.K. Tse

    (National University of Singapore)

  • Albert K.C. Tsui

    (National University of Singapore)

Abstract

In this paper we propose a new multivariate GARCH model with time- varying correlations. We adopt the vech representation based on the conditional variances and the conditional correlations. While each conditional-variance term is assumed to follow a univariate GARCH formulation, the conditional-correlation matrix is postulated to follow an autoregressive moving average type of analogue. By imposing some suitable restrictions on the conditional-correlation-matrix equation, we construct a MGARCH model in which the conditional-correlation matrix is guaranteed to be positive definite during the optimisation. Thus, our new model retains the intuition and interpretation of the univariate GARCH model and yet satisfies the positive-definite condition as found in the constant-correlation and BEKK models. We report some Monte Carlo results on the finite-sample distributions of the MLE of the varying- correlation MGARCH model. The new model is applied to some real data sets. It is found that extending the constant-correlation model to allow for time-varying correlations provides some interesting time histories that are not available in a constant-correlation model.

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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0004007.

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Length: 30 pages
Date of creation: 06 Nov 2000
Date of revision:
Handle: RePEc:wpa:wuwpem:0004007

Note: Type of Document - Acrobat PDF; prepared on SW2.5; to print on HP/PostScript/Franciscan monk; pages: 30; figures: included
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Web page: http://128.118.178.162

Related research

Keywords: BEKK models; constant correlation; Monte Carlo method; multivariate GARCH model; maximum likelihood estimate; varying correlation;

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