Essec\Faculty\Model\Contribution {#2233
  #_index: "academ_contributions"
  #_id: "14840"
  #_source: array:26 [
    "id" => "14840"
    "slug" => "estimating-and-testing-long-run-risk-models-international-evidence"
    "yearMonth" => "2024-08"
    "year" => "2024"
    "title" => "Estimating and Testing Long-Run Risk Models: International Evidence"
    "description" => "FULOP, A., LI, J., LIU, H. et YAN, C. (2024). Estimating and Testing Long-Run Risk Models: International Evidence. <i>Management Science</i>, In press."
    "authors" => array:4 [
      0 => array:3 [
        "name" => "FULOP Andras"
        "bid" => "B00072302"
        "slug" => "fulop-andras"
      ]
      1 => array:1 [
        "name" => "LI Junye"
      ]
      2 => array:1 [
        "name" => "LIU Hening"
      ]
      3 => array:1 [
        "name" => "YAN Cheng"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:1 [
      0 => "Long-Run Risk Models"
    ]
    "updatedAt" => "2024-10-31 13:51:19"
    "publicationUrl" => "https://doi.org/10.1287/mnsc.2022.04054"
    "publicationInfo" => array:3 [
      "pages" => null
      "volume" => "In press"
      "number" => null
    ]
    "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 estimate and test long-run risk models using international macroeconomic and financial data. The benchmark model features a representative agent who has recursive preferences with a time preference shock, a persistent component in expected consumption growth, and stochastic volatility in fundamentals characterized by an autoregressive gamma process. We construct a comprehensive data set with quarterly frequency for 10 developed countries and employ an efficient likelihood-based Bayesian method that exploits up-to-date sequential Monte Carlo methods to make full econometric inference. Our empirical findings provide international evidence in support of long-run risks, time-varying preference shocks, and countercyclicality of the stochastic discount factor. We show the existence of a global long-run consumption factor driving equity returns across individual countries."
      "en" => "We estimate and test long-run risk models using international macroeconomic and financial data. The benchmark model features a representative agent who has recursive preferences with a time preference shock, a persistent component in expected consumption growth, and stochastic volatility in fundamentals characterized by an autoregressive gamma process. We construct a comprehensive data set with quarterly frequency for 10 developed countries and employ an efficient likelihood-based Bayesian method that exploits up-to-date sequential Monte Carlo methods to make full econometric inference. Our empirical findings provide international evidence in support of long-run risks, time-varying preference shocks, and countercyclicality of the stochastic discount factor. We show the existence of a global long-run consumption factor driving equity returns across individual countries."
    ]
    "authors_fields" => array:2 [
      "fr" => "Finance"
      "en" => "Finance"
    ]
    "indexedAt" => "2024-12-21T18:21:44.000Z"
    "docTitle" => "Estimating and Testing Long-Run Risk Models: International Evidence"
    "docSurtitle" => "Articles"
    "authorNames" => "<a href="/cv/fulop-andras">FULOP Andras</a>, LI Junye, LIU Hening, YAN Cheng"
    "docDescription" => "<span class="document-property-authors">FULOP Andras, LI Junye, LIU Hening, YAN Cheng</span><br><span class="document-property-authors_fields">Finance</span> | <span class="document-property-year">2024</span>"
    "keywordList" => "<a href="#">Long-Run Risk Models</a>"
    "docPreview" => "<b>Estimating and Testing Long-Run Risk Models: International Evidence</b><br><span>2024-08 | Articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1287/mnsc.2022.04054" target="_blank">Estimating and Testing Long-Run Risk Models: International Evidence</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.630022
  +"parent": null
}