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Dynamic Conditional Correlations for Asymmetric Processes


Asai, Manabu y McAleer, Michael (2011) Dynamic Conditional Correlations for Asymmetric Processes. [ Documentos de Trabajo del Instituto Complutense de Análisis Económico; nº 30, 2011, ] (No publicado)

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The paper develops two Dynamic Conditional Correlation (DCC) models, namely the Wishart DCC (wDCC) model. The paper applies the wDCC approach to the exponential GARCH (EGARCH) and GJR models to propose asymmetric DCC models. We use the standardized multivariate t-distribution to accommodate heavy-tailed errors. The paper presents an empirical example using the trivariate data of the Nikkei 225, Hang Seng and Straits Times Indices for estimating and forecasting the wDCC-EGARCH and wDCC-GJR models, and compares the performance with the asymmetric BEKK model. The empirical results show that AIC and BIC favour the wDCC-EGARCH model to the wDCC-GJR, asymmetric BEKK and alternative conventional DCC models. Moreover, the empirical results indicate that the wDCC-EGARCH-t model produces reasonable VaR threshold forecasts, which are very close to the nominal 1% to 3% values.

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

The authors wish to thank the editor and two referees for insightful comments and suggestions and Yoshi Baba for helpful discussions. For financial support, the first author acknowledges the Japan Society for the Promotion of Science and the Australian Academy of Science, and the second author wishes to acknowledge the Australian Research Council, National Science Council, Taiwan, and the Japan Society for the Promotion of Science.

Palabras clave:Dynamic conditional correlations, Wishart process, EGARCH, GJR, asymmetric BEKK, heavy-tailed errors.
Materias:Ciencias Sociales > Economía > Econometría
Título de serie o colección:Documentos de Trabajo del Instituto Complutense de Análisis Económico
Código ID:13216

Asai, M. and M. McAleer (2008), “A Portfolio Index GARCH Model”, International Journal of Forecasting, 24, 449-461.

Asai, M. and M. McAleer (2009), “The Structure of Dynamic Correlations in Multivariate Stochastic Volatility Models”, Journal of Econometrics, 150, 182-192.

Asai, M., M. McAleer, and J. Yu (2006), “Multivariate Stochastic Volatility: A Review”, Econometric Reviews, 25, 145-175.

Bauwens L., S. Laurent and J.K.V. Rombouts (2006), “Multivariate GARCH Models: A Survey”, Journal of Applied Econometrics, 21, 79-109.

Bollerslev, T. (1990), “Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Approach”, Review of Economics and Statistics, 72, 498-505.

Bollerslev, T., R.F. Engle and J.M. Wooldridge (1988), “A Capital Asset Pricing Model with Time Varying Covariances”, Journal of Political Economy, 96, 116-131.

Bollerslev, T. and J.M. Wooldridge (1992), “Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time-Varying Covariances”, Econometric Reviews, 11, 143--172.

Chiu, T.Y.M, T. Leonard and K.-W. Tsui (1996), “The Matrix-Logarithmic Covariance Model”, Journal of the American Statistical Association, 91, 198-210.

Deb, P. (1996), “Finite Sample Properties of the Maximum Likelihood Estimator of EGARCH Models”, Econometric Reviews, 15, 51-68.

Ding, Z. and R.F. Engle (2001), “Large Scale Conditional Covariance Matrix Modeling, Estimation and Testing”, Academia Economic Papers, 1, 83-106.

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 K.F. Kroner (1995), “Multivariate Simultaneous Generalized ARCH”, Econometric Theory, 11, 122-150.

Glosten, L., R. Jagannathan and D. Runkle (1992), “On the Relation Between the Expected Value and Volatility of Nominal Excess Returns on Stocks”, Journal of Finance, 46, 1779-1801.

Hentschel, L. (1995), “All in the Family: Nesting Symmetric and Asymmetric GARCH Models”, Journal of Financial Economics, 39, 71-104.

Kawakatsu, H., (2006), “Matrix Exponential GARCH”, Journal of Econometrics, 134, 95-128.

Kroner, K. and V. Ng (1998), “Modeling Asymmetric Comovements of Asset Returns”, Review of Financial Studies, 11, 817-844.

Ling, S. and M. McAleer (2003), “On Adaptive Estimation in Nonstationary ARMA Models with GARCH Errors”, Annals of Statistics, 31, 642-674.

Lumsdaine, R.L. (1995), “Finite-Sample Properties of the Maximum Likelihood Estimator in GARCH(1,1) and IGARCH(1,1) Models: A Monte Carlo Investigation”, Journal of Business & Economics Statistics, 13, 1--10.

McAleer, M. (2005), “Automated Inference and Learning in Modeling Financial Volatility”, Econometric Theory, 21, 232-261.

McAleer, M., F. Chan, S. Hoti and O. Lieberman (2008), “Generalized Autoregressive Conditional Correlation”, Econometric Theory, 24, 1554-1583.

McAleer, M., S. Hoti, and F. Chan (2009), “Structure and Asymptotic Theory for Multivariate Asymmetric Conditional Volatility,” Econometric Reviews, 28, 422-440.

Nelson, D.B. (1990), “ARCH Models as Diffusion Approximations”, Journal of Econometrics, 45, 7–38.

Nelson, D.B. (1991), “Conditional Heteroskedasticity in Asset Returns: A New Approach”, Econometrica, 59, 347-370.

Song, P. X-K., Y. Fan, and J.D. Kalbfleisch (2005), “Maximization by Parts in Likelihood Inference”, Journal American Statistical Association, 100, 1163-1167.

Tse, Y.K., and A.K.C. Tsui (2002), “A Multivariate GARCH Model with Time-Varying Correlations”, Journal of Business and Economic Statistics, 20, 351–362.

van der Weide, R. (2002), “GO-GARCH: A Multivariate Generalized Orthogonal GARCH Model”, Journal of Applied Econometrics, 17, 549-564.

Depositado:06 Sep 2011 10:00
Última Modificación:15 Nov 2013 10:49

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