Essec\Faculty\Model\Contribution {#2216
  #_index: "academ_contributions"
  #_id: "10416"
  #_source: array:26 [
    "id" => "10416"
    "slug" => "theory-and-inference-for-a-markov-switching-garch-model"
    "yearMonth" => "2010-07"
    "year" => "2010"
    "title" => "Theory and Inference for a Markov Switching GARCH Model"
    "description" => "BAUWENS, L., PREMINGER, A. et ROMBOUTS, J. (2010). Theory and Inference for a Markov Switching GARCH Model. <i>Econometrics Journal</i>, 13(2), pp. 218-244."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "ROMBOUTS Jeroen"
        "bid" => "B00469813"
        "slug" => "rombouts-jeroen"
      ]
      1 => array:1 [
        "name" => "BAUWENS Luc"
      ]
      2 => array:1 [
        "name" => "PREMINGER Arie"
      ]
    ]
    "ouvrage" => ""
    "keywords" => []
    "updatedAt" => "2021-07-13 14:31:34"
    "publicationUrl" => "https://doi.org/10.1111/j.1368-423X.2009.00307.x"
    "publicationInfo" => array:3 [
      "pages" => "218-244"
      "volume" => "13"
      "number" => "2"
    ]
    "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" => "We develop a Markov‐switching GARCH model (MS‐GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existence of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter space to include the state variables, Bayesian estimation using a Gibbs sampling algorithm is feasible. We illustrate the model on S&P500 daily returns."
      "en" => "We develop a Markov‐switching GARCH model (MS‐GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existence of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter space to include the state variables, Bayesian estimation using a Gibbs sampling algorithm is feasible. We illustrate the model on S&P500 daily 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" => "Theory and Inference for a Markov Switching GARCH Model"
    "docSurtitle" => "Journal articles"
    "authorNames" => "<a href="/cv/rombouts-jeroen">ROMBOUTS Jeroen</a>, BAUWENS Luc, PREMINGER Arie"
    "docDescription" => "<span class="document-property-authors">ROMBOUTS Jeroen, BAUWENS Luc, PREMINGER Arie</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2010</span>"
    "keywordList" => ""
    "docPreview" => "<b>Theory and Inference for a Markov Switching GARCH Model</b><br><span>2010-07 | Journal articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1111/j.1368-423X.2009.00307.x" target="_blank">Theory and Inference for a Markov Switching GARCH Model</a>"
  ]
  +lang: "en"
  +"_type": "_doc"
  +"_score": 8.758286
  +"parent": null
}