English, Article, Journal or magazine article edition: Multivariate mixed normal conditional heteroskedasticity Luc, BAUWENS; C.M., HAFNER; J.V.K., ROMBOUTS

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Bookmark: http://trove.nla.gov.au/version/177964
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Multivariate mixed normal conditional heteroskedasticity
Author
  • Luc, BAUWENS
  • C.M., HAFNER
  • J.V.K., ROMBOUTS
Physical Description
  • preprint
Notes
  • 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.
  • Multivariate volatility; Finite mixture; EM algorithm; Bayesian inference
  • RePEc:ctl:louvec:2006007
Language
  • English
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