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2006 | 7 | 59--67
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Measuring Conditional Dependence of Polish Financial Returns

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In this paper we apply C-MGARCH methodology to model conditional dependency between pairs of selected Polish financial returns. We compare the dynamic conditional correlations estimated by means of C-DCC model belong-ing to the C-MGARCH class with those obtained with Engle's DCC model (Engle 2002). We also compare the 1-day ahead conditional correlation fore-casts calculated with DCC and C-DCC models. In addition, by using Euclidean matrix norm, we evaluated how the implied conditional covariance forecasts fit the matrices of cross products of actually realized daily returns. Our main find-ing is that the conditional correlations obtained with the applied C-DCC model (for all the considered pairs they are almost everywhere positive) are, in fact totally, much lower than the ones estimated with Engle's DCC model. As re-gards the point forecasts of matrices of cross products of daily returns, we find that DCC models are better in that task but, altogether, we find the results not very impressive.(fragment of text)
Opis fizyczny
  • Adam Mickiewicz University in Poznań, Poland
  • Andersen, T.G., Bollerslev, T., Christoffersen, P.F., Diebold, F.X. (2005), Practical Volatility and Correlations Modeling for Financial Market Risk Management, Penn Institute for Economic Research Working Paper 05-007.
  • Bauwens, E. Laurent, S. Rombouts, J.V.K. (2003), Multivariate GARCH Models: A Survey, Core Discussion Paper 2003/1.
  • Engle, R.F. (2002), Dynamic Conditional Correlation: A Simple Class of Multivariate GARCH Models, Journal of Business and Economic Statistics 20, 339-350.
  • Lee, T.H., Long, X. (2005), Copula-based Multivariate GARCH Model with Uncorrelated Dependent Standardized Returns, Department of Economics, University of California, Riverside.
  • Lehmann, E.L. (1966), Some Concepts of Dependence, Annals of Mathematical Statistics, 37, 1137-1153.
  • Nelsen, R.B. (1999), An Introduction to Copulas, Springer Verlag, New York.
  • Sklar, A. (1959), Fonctions de repartition à n dimensions et leurs marges, Publicatons de Institut Statistique de Universite de Paris 8, 229-231.
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