Multivariate normal mixture GARCH
We present a multivariate generalization of the mixed normal GARCH model proposed in Haas, Mittnik, and Paolella (2004a). Issues of parametrization and estimation are discussed. We derive conditions for covariance stationarity and the existence of the fourth moment, and provide expressions for the dynamic correlation structure of the process. These results are also applicable to the single-component multivariate GARCH(p, q) model and simplify the results existing in the literature. In an application to stock returns, we show that the disaggregation of the conditional (co)variance process generated by our model provides substantial intuition, and we highlight a number of findings with potential significance for portfolio selection and further financial applications, such as regime-dependent correlation structures and leverage effects.
|Date of creation:||2006|
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- Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
- Bauwens, Luc & Laurent, Sebastien, 2005.
"A New Class of Multivariate Skew Densities, With Application to Generalized Autoregressive Conditional Heteroscedasticity Models,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 23, pages 346-354, July.
- BAUWENS, Luc & LAURENT, Sébastien, "undated". "A new class of multivariate skew densities, with application to generalized autoregressive conditional heteroscedasticity models," CORE Discussion Papers RP 1793, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Tom Doan, "undated". "LOGMVSKEWT: RATS procedure to compute function for log density of multivariate skew-t distribution," Statistical Software Components RTS00107, Boston College Department of Economics.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
- BAUWENS, Luc & LAURENT, Sébastien & ROMBOUTS, Jeroen VK, "undated". "Multivariate GARCH models: a survey," CORE Discussion Papers RP 1847, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- BAUWENS, Luc & LAURENT, Sébastien & ROMBOUTS, Jeroen, 2003. "Multivariate GARCH models: a survey," CORE Discussion Papers 2003031, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- M. Angeles Carnero, 2004. "Persistence and Kurtosis in GARCH and Stochastic Volatility Models," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(2), pages 319-342.
- Engle, Robert F & Ng, Victor K, 1993. " Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
- Robert F. Engle & Victor K. Ng, 1991. "Measuring and Testing the Impact of News on Volatility," NBER Working Papers 3681, National Bureau of Economic Research, Inc.
- Cai, Jun, 1994. "A Markov Model of Switching-Regime ARCH," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 309-316, July.
- Davidson, James, 2002. "Establishing conditions for the functional central limit theorem in nonlinear and semiparametric time series processes," Journal of Econometrics, Elsevier, vol. 106(2), pages 243-269, February. Full references (including those not matched with items on IDEAS)