Multivariate mixed normal conditional heteroskedasticity

Luc Bauwens, Christian Hafner and Jeroen Rombouts

No 2006012, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: We propose a new multivariate volatility model where the conditional distribution of a vector time series is given by a mixture of multivariate normal distributions. Each of these distributions is allowed to have a time-varying covariance matrix. The process can be globally covariance- stationary even though some components are not covariance-stationary. We derive some theo- retical properties of the model such as the unconditional covariance matrix and autocorrelations of squared returns. The complexity of the model requires a powerful estimation algorithm. In a simulation study we compare estimation by maximum likelihood with the EM algorithm and Bayesian estimation with a Gibbs sampler. Finally, we apply the model to daily U.S. stock returns.

Keywords: multivariate volatility; finite mixture; EM algorithm; Bayesian inference (search for similar items in EconPapers)
JEL-codes: C11 C22 C52 (search for similar items in EconPapers)
Date: 2006-02
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Multivariate mixed normal conditional heteroskedasticity (2007)
Working Paper: Multivariate mixed normal conditional heteroskedasticity (2007)
Working Paper: Multivariate mixed normal conditional heteroskedasticity (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:cor:louvco:2006012

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