Multivariate mixed normal conditional heteroskedasticity

Bauwens, Luc;Hafner, Christian;Rombouts, Jeroen
(2006)

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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 theoretical 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.
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Bauwens, L., Hafner, C., & Rombouts, J. (2006). Multivariate mixed normal conditional heteroskedasticity (ECON Discussion Papers 2006/07). https://hdl.handle.net/2078.5/128416