Essec\Faculty\Model\Contribution {#2233
  #_index: "academ_contributions"
  #_id: "15002"
  #_source: array:26 [
    "id" => "15002"
    "slug" => "realized-drift"
    "yearMonth" => "2024-07"
    "year" => "2024"
    "title" => "Realized drift"
    "description" => "LAURENT, S., RENO, R. et SHI, S. (2024). Realized drift. <i>Journal of Econometrics</i>, In press, pp. 105813."
    "authors" => array:3 [
      0 => array:3 [
        "name" => "RENO Roberto"
        "bid" => "B00798674"
        "slug" => "reno-roberto"
      ]
      1 => array:1 [
        "name" => "Laurent Sébastien"
      ]
      2 => array:1 [
        "name" => "Shi Shuping"
      ]
    ]
    "ouvrage" => ""
    "keywords" => array:4 [
      0 => "Drift"
      1 => "High-frequency Data"
      2 => "Serial Covariance"
      3 => "Volatility Forecasting"
    ]
    "updatedAt" => "2024-10-31 13:51:19"
    "publicationUrl" => "https://doi.org/10.1016/j.jeconom.2024.105813"
    "publicationInfo" => array:3 [
      "pages" => "105813"
      "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" => "Drift and volatility are two mainsprings of asset price dynamics. While volatilities have been studied extensively in the literature, drifts are commonly believed to be impossible to estimate and largely ignored in the literature. This paper shows how to detect drift using realized autocovariance implemented on high-frequency data. We use a theoretical treatment in which the classical model for the efficient price, an Itō semimartingale possibly contaminated by microstructure noise, is enriched with drift and volatility explosions. Our theory advocates a novel decomposition for realized variance into a drift and a volatility component, which leads to significant improvements in volatility forecasting."
      "en" => "Drift and volatility are two mainsprings of asset price dynamics. While volatilities have been studied extensively in the literature, drifts are commonly believed to be impossible to estimate and largely ignored in the literature. This paper shows how to detect drift using realized autocovariance implemented on high-frequency data. We use a theoretical treatment in which the classical model for the efficient price, an Itō semimartingale possibly contaminated by microstructure noise, is enriched with drift and volatility explosions. Our theory advocates a novel decomposition for realized variance into a drift and a volatility component, which leads to significant improvements in volatility forecasting."
    ]
    "authors_fields" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "indexedAt" => "2024-11-21T18:21:43.000Z"
    "docTitle" => "Realized drift"
    "docSurtitle" => "Articles"
    "authorNames" => "<a href="/cv/reno-roberto">RENO Roberto</a>, Laurent Sébastien, Shi Shuping"
    "docDescription" => "<span class="document-property-authors">RENO Roberto, Laurent Sébastien, Shi Shuping</span><br><span class="document-property-authors_fields">Systèmes d'Information, Data Analytics et Opérations</span> | <span class="document-property-year">2024</span>"
    "keywordList" => "<a href="#">Drift</a>, <a href="#">High-frequency Data</a>, <a href="#">Serial Covariance</a>, <a href="#">Volatility Forecasting</a>"
    "docPreview" => "<b>Realized drift</b><br><span>2024-07 | Articles </span>"
    "docType" => "research"
    "publicationLink" => "<a href="https://doi.org/10.1016/j.jeconom.2024.105813" target="_blank">Realized drift</a>"
  ]
  +lang: "fr"
  +"_type": "_doc"
  +"_score": 8.446512
  +"parent": null
}