Biblioteca de la Universidad Complutense de Madrid

Do We Really Need Both BEKK and DCC? A Tale of Two Covariance Models


Caporin, Massimiliano y McAleer, Michael (2009) Do We Really Need Both BEKK and DCC? A Tale of Two Covariance Models. [ Documentos de trabajo del Instituto Complutense de Análisis Económico (ICAE); nº 0904, 2009, ] (No publicado)

[img] PDF

URL Oficial:


Large and very large portfolios of financial assets are routine for many individuals and organizations. The two most widely used models of conditional covariances and correlations are BEKK and DCC. BEKK suffers from the archetypal "curse of dimensionality" whereas DCC does not. This is a misleading interpretation of the suitability of the two models to be used in practice. The primary purposes of the paper are to define targeting as an aid in estimating matrices associated with large numbers of financial assets, analyze the similarities and dissimilarities between BEKK and DCC, both with and without targeting, on the basis of structural derivation, the analytical forms of the sufficient conditions for the existence of moments, and the sufficient conditions for consistency and asymptotic normality, and computational tractability for very large (that is, ultra high) numbers of financial assets, to present a consistent two step estimation method for the DCC model, and to determine whether BEKK or DCC should be preferred in practical applications.

Tipo de documento:Documento de trabajo o Informe técnico
Información Adicional:

JEL Codes: G11, G33, C32
Massimiliano Caporin pertenece al Dipartimento di Scienze Economiche “Marco Fanno”, Università degli Studi di Padova,Facoltà di Scienze Statistiche.

Palabras clave:Conditional correlations, Conditional covariances, Diagonal models, Forecasting, Generalized models, Hadamard models, Scalar models, Targeting.
Materias:Ciencias > Estadística > Estadística Matemática
Título de serie o colección:Documentos de trabajo del Instituto Complutense de Análisis Económico (ICAE)
Código ID:8590

Aielli, 2008, Consistent estimation of large scale dynamic conditional correlations, University of Messina, Department of Economics, Statistics, Mathematics and Sociology, Working paper n. 47.

Asai, M., Caporin, M., and McAleer, M., 2009, Block structure multivariate stochastic volatility models, unpublished manuscript Bauwens L., Laurent, S. and Rombouts, J.K.V., 2006, Multivariate GARCH models: a survey, Journal of Applied Econometrics, 21, 79-109.

Billio, M. and Caporin M., 2009, A generalised dynamic conditional correlation model for portfolio risk evaluation, Mathematics and Computers in Simulations, forthcoming.

Billio, M., Caporin, M. and Gobbo, M., 2006, Flexible dynamic conditional correlation multivariate GARCH for asset allocation, Applied Financial Economics Letters, 2, 123-130.

Bollerslev T., 1990, Modelling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH approach, Review of Economic and Statistics, 72, 498-505.

Bonato, M., Caporin, M., and Ranaldo, A., 2009, Forecasting realized (co)variances with a block structure Wishart autoregressive model, Swiss National Bank Working Paper.

Bougerol, P. And Picard, N., 1992, Stationarity of GARCH processes, Journal of Econometrics, 52, 115-127.

Caporin, M. And McAleer, M., 2008, Scalar BEKK and Indirect DCC, Journal of Forecasting, 27-6, 537-549.

Caporin, M., and Paruolo, P., 2009, Structured multivariate volatility models, Department of Economics “Marco Fanno”, University of Padova, Working Paper 90.

Cappiello L., Engle, R.F. And Sheppard, K., 2006, Asymmetric dynamics in the correlations of global equity and bond returns, Journal of Financial Econometrics, 4, 537-572.

Comte, F. and Lieberman, O., 2003, Asymptotic theory for multivariate GARCH processes, Journal of Multivariate Analysis, 84, 61-84.

Ding, Z. and Engle, R., 2001, Large scale conditional covariance modelling, estimation and testing. Academia Economic Papers, 29, 157-184.

Engle, R.F., 2002, Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models, Journal of Business and Economic Statistics, 20, 339-350.

Engle, R.F. and Kroner, K.F., 1995, Multivariate simultaneous generalized ARCH, Econometric Theory, 11, 122-150.

Engle, R.F., and Sheppard, K., 2001, Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH, Working Paper 2001-15, University of California at San Diego.

Engle, R.F., Shephard, N., and Sheppard, K., 2008, Fitting vast dimensional time-varying covariance models, Oxford Financial Research Centre, Financial Economics Working Paper n. 30.

Glosten, L.R., Jagannathan, R. and Runkle, D.E., 1992, On the relation between the expected value and volatility of the nominal excess return on stocks, Journal of Finance, 46, 1779-1801.

Jeantheau, T., 1998, Strong consistency of estimators for multivariate ARCH models, Econometric Theory, 14, 70-86.

Ling, S. and McAleer, M., 2003, Asymptotic theory for a vector ARMA-GARCH model, Econometric Theory, 19, 278-308.

McAleer, M., 2005, Automated inference and learning in modeling financial volatility, Econometric Theory, 21, 232-261.

McAleer, M., Chan, F., Hoti, S., and Lieberman, O., 2008, Generalized autoregressive conditional correlation, Econometric Theory, 24-6, 1554-1583.

Nelson, D.B., 1991, Conditional heteroskedasticity in asset pricing: a new approach, Econometrica, 59, 347-370.

Newey, W.K. and D. McFadden, 1994, Large Sample Estimation and Hypothesis Testing, in Handbook of Econometrics, Vol. 4, Elsevier North-Holland.

Nicholls, D.F. and Quinn, B.G., 1982, Random Coefficient Autoregressive Models: An Introduction, Springer-Verlag, New York.

Tsay, R.S., 1987, Conditional heteroscedastic time series models, Journal of the American Statistical Association, 82, 590-604.

Depositado:03 Mar 2009 10:11
Última Modificación:15 Nov 2013 10:49

Sólo personal del repositorio: página de control del artículo