Multivariate mixed normal conditional heteroskedasticity

Luc Bauwens, Hafner C.M. and Jeroen Rombouts

No 2006007, Discussion Papers (ECON - Département des Sciences Economiques) from Université catholique de Louvain, Département des Sciences Economiques

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 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 a 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)
Pages: 23
Date: 2006-02-01
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (4)

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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:ctl:louvec:2006007

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