Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "10207"
  #_source: array:26 [
    "id" => "10207"
    "slug" => "multivariate-mixed-normal-conditional-heteroskedasticity"
    "yearMonth" => "2007-04"
    "year" => "2007"
    "title" => "Multivariate mixed normal conditional heteroskedasticity"
    "description" => "BAUWENS, L., HAFNER, C. et ROMBOUTS, J. (2007). Multivariate mixed normal conditional heteroskedasticity. <i>Computational Statistics and Data Analysis</i>, 51(7), pp. 3551-3566."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "ROMBOUTS Jeroen"
        "bid" => "B00469813"
        "slug" => "rombouts-jeroen"
      ]
      1 => array:1 [
        "name" => "BAUWENS Luc"
      ]
      2 => array:1 [
        "name" => "HAFNER Christian"
      ]
    ]
    "ouvrage" => ""
    "keywords" => []
    "updatedAt" => "2022-11-28 11:01:15"
    "publicationUrl" => "https://doi.org/10.1016/j.csda.2006.10.012"
    "publicationInfo" => array:3 [
      "pages" => "3551-3566"
      "volume" => "51"
      "number" => "7"
    ]
    "type" => array:2 [
      "fr" => "Articles"
      "en" => "Journal articles"
    ]
    "support_type" => array:2 [
      "fr" => "Revue scientifique"
      "en" => "Scientific journal"
    ]
    "countries" => array:2 [
      "fr" => null
      "en" => null
    ]
    "abstract" => array:2 [
      "fr" => "A new multivariate volatility model where the conditional distribution of a vector time series is given by a mixture of multivariate normal distributions is proposed. 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. Some theoretical properties of the model such as the unconditional covariance matrix and autocorrelations of squared returns are derived. The complexity of the model requires a powerful estimation algorithm. A simulation study compares estimation by maximum likelihood with the EM algorithm. Finally, the model is applied to daily US stock returns."
      "en" => "A new multivariate volatility model where the conditional distribution of a vector time series is given by a mixture of multivariate normal distributions is proposed. 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. Some theoretical properties of the model such as the unconditional covariance matrix and autocorrelations of squared returns are derived. The complexity of the model requires a powerful estimation algorithm. A simulation study compares estimation by maximum likelihood with the EM algorithm. Finally, the model is applied to daily US stock returns."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-12-21T16:21:43.000Z"
    "docTitle" => "Multivariate mixed normal conditional heteroskedasticity"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/rombouts-jeroen">ROMBOUTS Jeroen</a>, BAUWENS Luc, HAFNER Christian"
    "docDescription" => "<span class="document-property-authors">ROMBOUTS Jeroen, BAUWENS Luc, HAFNER Christian</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2007</span>"
    "keywordList" => ""
    "docPreview" => "<b>Multivariate mixed normal conditional heteroskedasticity</b><br><span>2007-04 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1016/j.csda.2006.10.012" target="_blank">Multivariate mixed normal conditional heteroskedasticity</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.758286
  +"parent": null
}