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A Multivariate GARCH Model with Time-Varying correlations

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Author Info
Y. K. Tse (National University of Singapore)
Albert K. C. Tsui (National University of Singapore)

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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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Paper provided by EconWPA in its series Econometrics with number 0004010.

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Length: 30 pages
Date of creation: 18 Nov 2000
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Handle: RePEc:wpa:wuwpem:0004010

Note: Type of Document - PDF; prepared on PC; to print on Postscript; pages: 30; figures: included
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Related research
Keywords: BEKK model; constant correlation; Monte Carlo method; multivariate GARCH model; maximum likelihood estimate; varying correlation;

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Find related papers by JEL classification:
C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing

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    Other versions:
  3. Kroner, Kenneth F. & Claessens, Stijn, 1991. "Optimal dynamic hedging portfolios and the currency composition of external debt," Journal of International Money and Finance, Elsevier, vol. 10(1), pages 131-148, March. [Downloadable!] (restricted)
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  5. Baillie, Richard T & Myers, Robert J, 1991. "Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(2), pages 109-24, April-Jun. [Downloadable!] (restricted)
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  8. Kee-Hong Bae & G. Andrew Karolyi & Rene M. Stulz, 2000. "A New Approach to Measuring Financial Contagion," NBER Working Papers 7913, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  9. Engle, Robert F. & Granger, C. W. J. & Kraft, Dennis, 1984. "Combining competing forecasts of inflation using a bivariate arch model," Journal of Economic Dynamics and Control, Elsevier, vol. 8(2), pages 151-165, November. [Downloadable!] (restricted)
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  12. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59. [Downloadable!] (restricted)
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