Essec\Faculty\Model\Profile {#2233
  #_id: "B00469813"
  #_source: array:40 [
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    "academId" => "2084"
    "slug" => "rombouts-jeroen"
    "fullName" => "Jeroen ROMBOUTS"
    "lastName" => "ROMBOUTS"
    "firstName" => "Jeroen"
    "title" => array:2 [
      "fr" => "Professeur"
      "en" => "Professor"
    ]
    "email" => "rombouts@essec.edu"
    "status" => "ACTIF"
    "campus" => "Campus de Cergy"
    "departments" => []
    "phone" => "+33 (0)1 34 43 30 49"
    "sites" => []
    "facNumber" => "2084"
    "externalCvUrl" => "https://faculty.essec.edu/cv/rombouts-jeroen/pdf"
    "googleScholarUrl" => "https://scholar.google.com/citations?user=XAKQzRgAAAAJ"
    "facOrcId" => "https://orcid.org/0000-0003-4255-9227"
    "career" => array:20 [
      0 => Essec\Faculty\Model\CareerItem {#2247
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          "label" => array:2 [
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            "en" => "ESSEC Business School"
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      }
      1 => Essec\Faculty\Model\CareerItem {#2248
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        +"parent": Essec\Faculty\Model\Profile {#2233}
      }
      2 => Essec\Faculty\Model\CareerItem {#2249
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        +"parent": Essec\Faculty\Model\Profile {#2233}
      }
      3 => Essec\Faculty\Model\CareerItem {#2250
        #_index: null
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            "en" => "Chaired professor of the Chair Accenture Strategic Business Analytics"
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        +"parent": Essec\Faculty\Model\Profile {#2233}
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      4 => Essec\Faculty\Model\CareerItem {#2251
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      5 => Essec\Faculty\Model\CareerItem {#2252
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          ]
          "country" => array:2 [
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      }
      6 => Essec\Faculty\Model\CareerItem {#2253
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        +"parent": Essec\Faculty\Model\Profile {#2233}
      }
      7 => Essec\Faculty\Model\CareerItem {#2254
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      8 => Essec\Faculty\Model\CareerItem {#2255
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        +"parent": Essec\Faculty\Model\Profile {#2233}
      }
      9 => Essec\Faculty\Model\CareerItem {#2256
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        +"parent": Essec\Faculty\Model\Profile {#2233}
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      10 => Essec\Faculty\Model\CareerItem {#2257
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          ]
          "country" => array:2 [
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          ]
        ]
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        +"parent": Essec\Faculty\Model\Profile {#2233}
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      11 => Essec\Faculty\Model\CareerItem {#2258
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      12 => Essec\Faculty\Model\CareerItem {#2259
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      13 => Essec\Faculty\Model\CareerItem {#2260
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      14 => Essec\Faculty\Model\CareerItem {#2261
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      15 => Essec\Faculty\Model\CareerItem {#2262
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      16 => Essec\Faculty\Model\CareerItem {#2263
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            "fr" => "États-Unis"
            "en" => "United States of America"
          ]
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        +"parent": Essec\Faculty\Model\Profile {#2233}
      }
      17 => Essec\Faculty\Model\CareerItem {#2264
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            "fr" => "États-Unis"
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      18 => Essec\Faculty\Model\CareerItem {#2265
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      }
      19 => Essec\Faculty\Model\CareerItem {#2266
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      0 => Essec\Faculty\Model\Diplome {#2235
        #_index: null
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          "label" => array:2 [
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            "fr" => "Ph.D. en Econométrie"
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            "en" => "Université Catholique de Louvain"
          ]
          "country" => array:2 [
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            "en" => "Belgium"
          ]
        ]
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        +"parent": Essec\Faculty\Model\Profile {#2233}
      }
      1 => Essec\Faculty\Model\Diplome {#2237
        #_index: null
        #_id: null
        #_source: array:6 [
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          "label" => array:2 [
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      2 => Essec\Faculty\Model\Diplome {#2234
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        #_source: array:6 [
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            "en" => "Université Catholique de Louvain"
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      3 => Essec\Faculty\Model\Diplome {#2238
        #_index: null
        #_id: null
        #_source: array:6 [
          "diplome" => "DIPLOMA"
          "type" => array:2 [
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          "label" => array:2 [
            "en" => "Master in Economics"
            "fr" => "Master en Economie"
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          "institution" => array:2 [
            "fr" => "Katholieke Universiteit Leuven"
            "en" => "Katholieke Universiteit Leuven"
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    "bio" => array:2 [
      "fr" => """
        <p>Ph.D. In Econometrics, CORE, Universite Catholique de Louvain. Currently CORE research associate and Elected Member of the International Statistical Institute. Joined ESSEC in 2013 and formerly professor at HEC Montreal (2004-2012).</p>\n
        \n
        <p>Professor Jeroen VK Rombouts joined ESSEC Business School in January 2013. He combines rich data sets with statistical tools to answer financial questions. His research outcomes are published in international peer reviewed journals. He teaches courses of Basic Statistics and Econometrics in the Master and PhD programs. He has been Visiting Scholar at several universities (University of Pittsburg, Tilburg University, Erasmus University Rotterdam, Aarhus University (CREATES) and CORE as a research associate among others). He is an expert consultant in financial econometrics and macro-economic forecasting. Prior to joining ESSEC Business School, Jeroen was Associate Professor at HEC Montreal (2004-2012)</p>
        """
      "en" => "Ph.D. In Econometrics, CORE, Universite Catholique de Louvain. Currently CORE research associate and Elected Member of the International Statistical Institute. Joined ESSEC in 2013 and formerly professor at HEC Montreal (2004-2012). </p>Professor Jeroen VK Rombouts joined ESSEC Business School in January 2013. He combines rich data sets with statistical tools to answer financial questions. His research outcomes are published in international peer reviewed journals. He teaches courses of Basic Statistics and Econometrics in the Master and PhD programs. He has been Visiting Scholar at several universities (University of Pittsburg, Tilburg University, Erasmus University Rotterdam, Aarhus University (CREATES) and CORE as a research associate among others). He is an expert consultant in financial econometrics and macro-economic forecasting. Prior to joining ESSEC Business School, Jeroen was Associate Professor at HEC Montreal (2004-2012)"
    ]
    "department" => array:2 [
      "fr" => "Systèmes d'Information, Data Analytics et Opérations"
      "en" => "Information Systems, Data Analytics and Operations"
    ]
    "site" => array:2 [
      "fr" => ""
      "en" => ""
    ]
    "industrrySectors" => array:2 [
      "fr" => "Marchés financiers"
      "en" => "Capital Markets"
    ]
    "researchFields" => array:2 [
      "fr" => "Econométrie - Statistiques - Finance"
      "en" => "Econometrics - Statistics - Finance"
    ]
    "teachingFields" => array:2 [
      "fr" => "Econométrie - Sciences de la décision - Théorie des probabilités et statistiques - Marchés financiers et institutions financières - Management - Marketing et analyses des données"
      "en" => "Econometrics - Decision Sciences - Probability Theory & Mathematical Statistics - Financial Markets & Institutions - Management - Marketing and Data Analytics"
    ]
    "distinctions" => []
    "teaching" => []
    "otherActivities" => array:10 [
      0 => Essec\Faculty\Model\ExtraActivity {#2232
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      }
      1 => Essec\Faculty\Model\ExtraActivity {#2236
        #_index: null
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            "fr" => null
            "en" => null
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        +"parent": Essec\Faculty\Model\Profile {#2233}
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      2 => Essec\Faculty\Model\ExtraActivity {#2239
        #_index: null
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        #_source: array:9 [
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      3 => Essec\Faculty\Model\ExtraActivity {#2240
        #_index: null
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        #_source: array:9 [
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            "fr" => null
            "en" => null
          ]
          "country" => array:2 [
            "fr" => null
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        +"parent": Essec\Faculty\Model\Profile {#2233}
      }
      4 => Essec\Faculty\Model\ExtraActivity {#2241
        #_index: null
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        #_source: array:9 [
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            "fr" => null
            "en" => null
          ]
          "country" => array:2 [
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      }
      5 => Essec\Faculty\Model\ExtraActivity {#2242
        #_index: null
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        #_source: array:9 [
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      6 => Essec\Faculty\Model\ExtraActivity {#2243
        #_index: null
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      7 => Essec\Faculty\Model\ExtraActivity {#2244
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      8 => Essec\Faculty\Model\ExtraActivity {#2245
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      9 => Essec\Faculty\Model\ExtraActivity {#2246
        #_index: null
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    "theses" => []
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    "contributions" => array:51 [
      0 => Essec\Faculty\Model\Contribution {#2268
        #_index: "academ_contributions"
        #_id: "712"
        #_source: array:18 [
          "id" => "712"
          "slug" => "bayesian-option-pricing-using-mixed-normal-heteroskedasticity-models"
          "yearMonth" => "2014-08"
          "year" => "2014"
          "title" => "Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models"
          "description" => "ROMBOUTS, J. et STENTOFT, L. (2014). Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models. <i>Computational Statistics and Data Analysis</i>, 76, pp. 588-605."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "STENTOFT L."
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:3 [
            0 => "Bayesian inference"
            1 => "Option pricing"
            2 => "Finite mixture models"
          ]
          "updatedAt" => "2021-02-02 16:16:18"
          "publicationUrl" => "https://www.sciencedirect.com/science/article/abs/pii/S0167947313002454"
          "publicationInfo" => array:3 [
            "pages" => "588-605"
            "volume" => "76"
            "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" => "Option pricing using mixed normal heteroscedasticity models is considered. It is explained how to perform inference and price options in a Bayesian framework. The approach allows to easily compute risk neutral predictive price densities which take into account parameter uncertainty. In an application to the S&P 500 index, classical and Bayesian inference is performed on the mixture model using the available return data. Comparing the ML estimates and posterior moments small differences are found. When pricing a rich sample of options on the index, both methods yield similar pricing errors measured in dollar and implied standard deviation losses, and it turns out that the impact of parameter uncertainty is minor. Therefore, when it comes to option pricing where large amounts of data are available, the choice of the inference method is unimportant. The results are robust to different specifications of the variance dynamics but show however that there might be scope for using Bayesian methods when considerably less data is available for inference."
            "en" => "Option pricing using mixed normal heteroscedasticity models is considered. It is explained how to perform inference and price options in a Bayesian framework. The approach allows to easily compute risk neutral predictive price densities which take into account parameter uncertainty. In an application to the S&P 500 index, classical and Bayesian inference is performed on the mixture model using the available return data. Comparing the ML estimates and posterior moments small differences are found. When pricing a rich sample of options on the index, both methods yield similar pricing errors measured in dollar and implied standard deviation losses, and it turns out that the impact of parameter uncertainty is minor. Therefore, when it comes to option pricing where large amounts of data are available, the choice of the inference method is unimportant. The results are robust to different specifications of the variance dynamics but show however that there might be scope for using Bayesian methods when considerably less data is available for inference."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      1 => Essec\Faculty\Model\Contribution {#2270
        #_index: "academ_contributions"
        #_id: "5961"
        #_source: array:18 [
          "id" => "5961"
          "slug" => "fast-density-estimation-in-graph-models"
          "yearMonth" => "2013-05"
          "year" => "2013"
          "title" => "Fast Density Estimation in Graph Models"
          "description" => "ROMBOUTS, J. (2013). Fast Density Estimation in Graph Models. Dans: CIREQ Econometrics Conference: Time Series and Financial Econometrics."
          "authors" => array:1 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
          ]
          "ouvrage" => "CIREQ Econometrics Conference: Time Series and Financial Econometrics"
          "keywords" => []
          "updatedAt" => "2021-04-19 17:57:25"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => null
            "volume" => null
            "number" => null
          ]
          "type" => array:2 [
            "fr" => "Communications dans une conférence"
            "en" => "Presentations at an Academic or Professional conference"
          ]
          "support_type" => array:2 [
            "fr" => null
            "en" => null
          ]
          "countries" => array:2 [
            "fr" => null
            "en" => null
          ]
          "abstract" => array:2 [
            "fr" => null
            "en" => null
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      2 => Essec\Faculty\Model\Contribution {#2272
        #_index: "academ_contributions"
        #_id: "2025"
        #_source: array:18 [
          "id" => "2025"
          "slug" => "marginal-likelihood-for-markov-switching-and-change-point-garch-models"
          "yearMonth" => "2014-01"
          "year" => "2014"
          "title" => "Marginal Likelihood for Markov-switching and Change-Point GARCH Models"
          "description" => "BAUWENS, L., DUFAYS, A. et ROMBOUTS, J. (2014). Marginal Likelihood for Markov-switching and Change-Point GARCH Models. <i>Journal of Econometrics</i>, 178(3), pp. 508-522."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "BAUWENS L."
            ]
            2 => array:1 [
              "name" => "DUFAYS Arnaud"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:7 [
            0 => "Bayesian inference"
            1 => "Simulation"
            2 => "GARCH"
            3 => "Markov-switching model"
            4 => "Change-point model"
            5 => "Marginal likelihood"
            6 => "Particle MCMC"
          ]
          "updatedAt" => "2021-02-02 16:16:18"
          "publicationUrl" => "https://www.sciencedirect.com/science/article/abs/pii/S030440761300167X"
          "publicationInfo" => array:3 [
            "pages" => "508-522"
            "volume" => "178"
            "number" => "3"
          ]
          "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" => "GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks in the volatility process. Flexible alternatives are Markov-switching GARCH and change-point GARCH models. They require estimation by MCMC methods due to the path dependence problem. An unsolved issue is the computation of their marginal likelihood, which is essential for determining the number of regimes or change-points. We solve the problem by using particle MCMC, a technique proposed by Andrieu et al. (2010). We examine the performance of this new method on simulated data, and we illustrate its use on several return series."
            "en" => "GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks in the volatility process. Flexible alternatives are Markov-switching GARCH and change-point GARCH models. They require estimation by MCMC methods due to the path dependence problem. An unsolved issue is the computation of their marginal likelihood, which is essential for determining the number of regimes or change-points. We solve the problem by using particle MCMC, a technique proposed by Andrieu et al. (2010). We examine the performance of this new method on simulated data, and we illustrate its use on several return series."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      3 => Essec\Faculty\Model\Contribution {#2269
        #_index: "academ_contributions"
        #_id: "2124"
        #_source: array:18 [
          "id" => "2124"
          "slug" => "on-loss-functions-and-ranking-forecasting-performances-of-multivariate-volatility-models"
          "yearMonth" => "2013-03"
          "year" => "2013"
          "title" => "On Loss Functions and Ranking Forecasting Performances of Multivariate Volatility Models"
          "description" => "LAURENT, G., ROMBOUTS, J. et VIOLANTE, F. (2013). On Loss Functions and Ranking Forecasting Performances of Multivariate Volatility Models. <i>Journal of Econometrics</i>, 173(1), pp. 1-10."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "LAURENT Gilles"
              "bid" => "B00770447"
              "slug" => "laurent-gilles"
            ]
            1 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            2 => array:1 [
              "name" => "VIOLANTE Francesco"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:5 [
            0 => "Volatility"
            1 => "Multivariate GARCH"
            2 => "Matrix norm"
            3 => "Loss function"
            4 => "Model confidence set"
          ]
          "updatedAt" => "2021-02-02 16:16:18"
          "publicationUrl" => "https://doi.org/10.1016/j.jeconom.2012.08.004"
          "publicationInfo" => array:3 [
            "pages" => "1-10"
            "volume" => "173"
            "number" => "1"
          ]
          "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" => "The ranking of multivariate volatility models is inherently problematic because when the unobservable volatility is substituted by a proxy, the ordering implied by a loss function may be biased with respect to the intended one. We point out that the size of the distortion is strictly tied to the level of the accuracy of the volatility proxy. We propose a generalized necessary and sufficient functional form for a class of non-metric distance measures of the Bregman type which ensure consistency of the ordering when the target is observed with noise. An application to three foreign exchange rates is provided."
            "en" => "The ranking of multivariate volatility models is inherently problematic because when the unobservable volatility is substituted by a proxy, the ordering implied by a loss function may be biased with respect to the intended one. We point out that the size of the distortion is strictly tied to the level of the accuracy of the volatility proxy. We propose a generalized necessary and sufficient functional form for a class of non-metric distance measures of the Bregman type which ensure consistency of the ordering when the target is observed with noise. An application to three foreign exchange rates is provided."
          ]
          "authors_fields" => array:2 [
            "fr" => "Finance"
            "en" => "Finance"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      4 => Essec\Faculty\Model\Contribution {#2273
        #_index: "academ_contributions"
        #_id: "2168"
        #_source: array:18 [
          "id" => "2168"
          "slug" => "option-pricing-with-asymmetric-heteroskedastic-normal-mixture-models"
          "yearMonth" => "2015-07"
          "year" => "2015"
          "title" => "Option Pricing with Asymmetric Heteroskedastic Normal Mixture Models"
          "description" => "ROMBOUTS, J. et STANTOFT, L. (2015). Option Pricing with Asymmetric Heteroskedastic Normal Mixture Models. <i>International Journal of Forecasting</i>, 31(3), pp. 635-650."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "STANTOFT L."
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:5 [
            0 => "Asymmetric heteroskedastic models"
            1 => "Finite mixture models"
            2 => "Option pricing"
            3 => "Out-of-sample prediction"
            4 => "Statistical fit"
          ]
          "updatedAt" => "2021-02-02 16:16:18"
          "publicationUrl" => "https://www.sciencedirect.com/science/article/abs/pii/S0169207014001782"
          "publicationInfo" => array:3 [
            "pages" => "635-650"
            "volume" => "31"
            "number" => "3"
          ]
          "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 propose an asymmetric GARCH in mean mixture model and provide a feasible way for option pricing within this general framework by deriving the appropriate risk neutral dynamics. We forecast out-of-sample prices of a large sample of options on the S\&P 500 index from January 2006 through December 2011 and compute dollar losses and implied standard deviation losses. We compare our results to existing mixture models and other benchmarks like component models and jump models. Using the model confidence set test, the overall dollar root mean squared error of the best performing benchmark model is significantly larger than the best mixture model."
            "en" => "We propose an asymmetric GARCH in mean mixture model and provide a feasible way for option pricing within this general framework by deriving the appropriate risk neutral dynamics. We forecast out-of-sample prices of a large sample of options on the S\&P 500 index from January 2006 through December 2011 and compute dollar losses and implied standard deviation losses. We compare our results to existing mixture models and other benchmarks like component models and jump models. Using the model confidence set test, the overall dollar root mean squared error of the best performing benchmark model is significantly larger than the best mixture model."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      5 => Essec\Faculty\Model\Contribution {#2267
        #_index: "academ_contributions"
        #_id: "6691"
        #_source: array:18 [
          "id" => "6691"
          "slug" => "mixtures-models-jumps-and-option-pricing"
          "yearMonth" => "2013-06"
          "year" => "2013"
          "title" => "Mixtures Models, Jumps and Option Pricing"
          "description" => "ROMBOUTS, J. et STENTOFT, L. (2013). Mixtures Models, Jumps and Option Pricing. Dans: 33rd International Symposium on Forecasting."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "STENTOFT L."
            ]
          ]
          "ouvrage" => "33rd International Symposium on Forecasting"
          "keywords" => []
          "updatedAt" => "2021-04-19 17:57:25"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => null
            "volume" => null
            "number" => null
          ]
          "type" => array:2 [
            "fr" => "Communications dans une conférence"
            "en" => "Presentations at an Academic or Professional conference"
          ]
          "support_type" => array:2 [
            "fr" => null
            "en" => null
          ]
          "countries" => array:2 [
            "fr" => null
            "en" => null
          ]
          "abstract" => array:2 [
            "fr" => null
            "en" => null
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      6 => Essec\Faculty\Model\Contribution {#2271
        #_index: "academ_contributions"
        #_id: "2459"
        #_source: array:18 [
          "id" => "2459"
          "slug" => "root-t-consistent-density-estimation-in-garch-models"
          "yearMonth" => "2016-05"
          "year" => "2016"
          "title" => "Root-T Consistent Density Estimation in GARCH Models"
          "description" => "DELAIGLE, A., MEISTER, A. et ROMBOUTS, J. (2016). Root-T Consistent Density Estimation in GARCH Models. <i>Journal of Econometrics</i>, 192(1), pp. 55-63."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "DELAIGLE A."
            ]
            2 => array:1 [
              "name" => "MEISTER A."
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2021-02-02 16:16:18"
          "publicationUrl" => "https://www.researchgate.net/publication/285658229_Root-T_consistent_density_estimation_in_GARCH_models"
          "publicationInfo" => array:3 [
            "pages" => "55-63"
            "volume" => "192"
            "number" => "1"
          ]
          "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 consider a new nonparametric estimator of the stationary density of the logarithm of the volatility of the GARCH(1,1) model. This problem is particularly challenging since this density is still unknown, even in cases where the model parameters are given. Although the volatility variables are only observed with multiplicative independent innovation errors with unknown density, we manage to construct a nonparametric procedure which estimates the log volatility density consistently. By carefully exploiting the specific GARCH dependence structure of the data, our iterative procedure even attains the striking parametric root-T convergence rate. As a by-product of our main results, we also derive new smoothness properties of the stationary density. Using numerical simulations, we illustrate the performance of our estimator, and we provide an application to financial data."
            "en" => "We consider a new nonparametric estimator of the stationary density of the logarithm of the volatility of the GARCH(1,1) model. This problem is particularly challenging since this density is still unknown, even in cases where the model parameters are given. Although the volatility variables are only observed with multiplicative independent innovation errors with unknown density, we manage to construct a nonparametric procedure which estimates the log volatility density consistently. By carefully exploiting the specific GARCH dependence structure of the data, our iterative procedure even attains the striking parametric root-T convergence rate. As a by-product of our main results, we also derive new smoothness properties of the stationary density. Using numerical simulations, we illustrate the performance of our estimator, and we provide an application to financial data."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      7 => Essec\Faculty\Model\Contribution {#2274
        #_index: "academ_contributions"
        #_id: "7046"
        #_source: array:18 [
          "id" => "7046"
          "slug" => "relevant-parameter-changes-in-structural-break-models"
          "yearMonth" => "2018-09"
          "year" => "2018"
          "title" => "Relevant Parameter Changes in Structural Break Models"
          "description" => "ROMBOUTS, J. (2018). Relevant Parameter Changes in Structural Break Models. Dans: 2018 Econometric Theory and Time Series Analysis (ETTSA) Workshop."
          "authors" => array:1 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
          ]
          "ouvrage" => "2018 Econometric Theory and Time Series Analysis (ETTSA) Workshop"
          "keywords" => []
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => null
            "volume" => null
            "number" => null
          ]
          "type" => array:2 [
            "fr" => "Communications dans une conférence"
            "en" => "Presentations at an Academic or Professional conference"
          ]
          "support_type" => array:2 [
            "fr" => null
            "en" => null
          ]
          "countries" => array:2 [
            "fr" => null
            "en" => null
          ]
          "abstract" => array:2 [
            "fr" => null
            "en" => null
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      8 => Essec\Faculty\Model\Contribution {#2275
        #_index: "academ_contributions"
        #_id: "2505"
        #_source: array:18 [
          "id" => "2505"
          "slug" => "sparse-change-point-har-models-for-realized-variance"
          "yearMonth" => "2019-01"
          "year" => "2019"
          "title" => "Sparse Change-point HAR Models for Realized Variance"
          "description" => "DUFAYS, A. et ROMBOUTS, J. (2019). Sparse Change-point HAR Models for Realized Variance. <i>Econometric Reviews</i>, 38."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "DUFAYS Arnaud"
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => null
            "volume" => "38"
            "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" => "Change-point time series specifications constitute flexible models that capture unknown structural changes by allowing for switches in the model parameters. Nevertheless most models suffer from an over-parametrization issue since typically only one latent state variable drives the switches in all parameters. This implies that all parameters have to change when a break happens. To gauge whether and where there are structural breaks in realized variance, we introduce the sparse change-point HAR model. The approach controls for model parsimony by limiting the number of parameters which evolve from one regime to another. Sparsity is achieved thanks to employing a nonstandard shrinkage prior distribution. We derive a Gibbs sampler for inferring the parameters of this process. Simulation studies illustrate the excellent performance of the sampler. Relying on this new framework, we study the stability of the HAR model using realized variance series of several major international indices between January 2000 and August 2015."
            "en" => "Change-point time series specifications constitute flexible models that capture unknown structural changes by allowing for switches in the model parameters. Nevertheless most models suffer from an over-parametrization issue since typically only one latent state variable drives the switches in all parameters. This implies that all parameters have to change when a break happens. To gauge whether and where there are structural breaks in realized variance, we introduce the sparse change-point HAR model. The approach controls for model parsimony by limiting the number of parameters which evolve from one regime to another. Sparsity is achieved thanks to employing a nonstandard shrinkage prior distribution. We derive a Gibbs sampler for inferring the parameters of this process. Simulation studies illustrate the excellent performance of the sampler. Relying on this new framework, we study the stability of the HAR model using realized variance series of several major international indices between January 2000 and August 2015."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      9 => Essec\Faculty\Model\Contribution {#2276
        #_index: "academ_contributions"
        #_id: "2611"
        #_source: array:18 [
          "id" => "2611"
          "slug" => "the-contribution-of-structural-break-models-to-forecasting-macroeconomic-series"
          "yearMonth" => "2015-06"
          "year" => "2015"
          "title" => "The Contribution of Structural Break Models to Forecasting Macroeconomic Series"
          "description" => "BAUWENS, L., KOOP, G., KOROBILIS, D. et ROMBOUTS, J. (2015). The Contribution of Structural Break Models to Forecasting Macroeconomic Series. <i>Journal of Applied Econometrics</i>, 30(4), pp. 596-620."
          "authors" => array:4 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "BAUWENS L."
            ]
            2 => array:1 [
              "name" => "KOOP G."
            ]
            3 => array:1 [
              "name" => "KOROBILIS D."
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2021-02-02 16:16:18"
          "publicationUrl" => "https://doi.org/10.1002/jae.2387"
          "publicationInfo" => array:3 [
            "pages" => "596-620"
            "volume" => "30"
            "number" => "4"
          ]
          "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" => "This paper compares the forecasting performance of models that have been proposed for forecasting in the presence of structural breaks. They differ in their treatment of the break process, the model applied in each regime and the out-of-sample probability of a break. In an extensive empirical evaluation, we demonstrate the presence of breaks and their importance for forecasting. We find no single model that consistently works best in the presence of breaks. In many cases, the formal modeling of the break process is important in achieving a good forecast performance. However, there are also many cases where rolling window forecasts perform well."
            "en" => "This paper compares the forecasting performance of models that have been proposed for forecasting in the presence of structural breaks. They differ in their treatment of the break process, the model applied in each regime and the out-of-sample probability of a break. In an extensive empirical evaluation, we demonstrate the presence of breaks and their importance for forecasting. We find no single model that consistently works best in the presence of breaks. In many cases, the formal modeling of the break process is important in achieving a good forecast performance. However, there are also many cases where rolling window forecasts perform well."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      10 => Essec\Faculty\Model\Contribution {#2277
        #_index: "academ_contributions"
        #_id: "2738"
        #_source: array:18 [
          "id" => "2738"
          "slug" => "the-value-of-multivariate-model-sophistication-an-application-to-pricing-dow-jones-industrial-average-options"
          "yearMonth" => "2014-01"
          "year" => "2014"
          "title" => "The Value of Multivariate Model Sophistication: An Application to Pricing Dow Jones Industrial Average Options"
          "description" => "ROMBOUTS, J., STENTOFT, L. et VIOLANTE, F. (2014). The Value of Multivariate Model Sophistication: An Application to Pricing Dow Jones Industrial Average Options. <i>International Journal of Forecasting</i>, 30(1), pp. 78-98."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "STENTOFT L."
            ]
            2 => array:1 [
              "name" => "VIOLANTE Francesco"
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2021-02-02 16:16:18"
          "publicationUrl" => "https://doi.org/10.1016/j.ijforecast.2013.07.006"
          "publicationInfo" => array:3 [
            "pages" => "78-98"
            "volume" => "30"
            "number" => "1"
          ]
          "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 assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their specification of the conditional variance, conditional correlation, innovation distribution, and estimation approach. All of the models belong to the dynamic conditional correlation class, which is particularly suitable because it allows consistent estimations of the risk neutral dynamics with a manageable amount of computational effort for relatively large scale problems. It turns out that increasing the sophistication in the marginal variance processes (i.e., nonlinearity, asymmetry and component structure) leads to important gains in pricing accuracy. Enriching the model with more complex existing correlation specifications does not improve the performance significantly. Estimating the standard dynamic conditional correlation model by composite likelihood, in order to take into account potential biases in the parameter estimates, generates only slightly better results. To enhance this poor performance of correlation models, we propose a new model that allows for correlation spillovers without too many parameters. This model performs about 60% better than the existing correlation models we consider. Relaxing a Gaussian innovation for a Laplace innovation assumption improves the pricing in a more minor way. In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances."
            "en" => "We assess the predictive accuracies of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set of 444 multivariate models that differ in their specification of the conditional variance, conditional correlation, innovation distribution, and estimation approach. All of the models belong to the dynamic conditional correlation class, which is particularly suitable because it allows consistent estimations of the risk neutral dynamics with a manageable amount of computational effort for relatively large scale problems. It turns out that increasing the sophistication in the marginal variance processes (i.e., nonlinearity, asymmetry and component structure) leads to important gains in pricing accuracy. Enriching the model with more complex existing correlation specifications does not improve the performance significantly. Estimating the standard dynamic conditional correlation model by composite likelihood, in order to take into account potential biases in the parameter estimates, generates only slightly better results. To enhance this poor performance of correlation models, we propose a new model that allows for correlation spillovers without too many parameters. This model performs about 60% better than the existing correlation models we consider. Relaxing a Gaussian innovation for a Laplace innovation assumption improves the pricing in a more minor way. In addition to investigating the value of model sophistication in terms of dollar losses directly, we also use the model confidence set approach to statistically infer the set of models that delivers the best pricing performances."
          ]
          "authors_fields" => array:2 [
            "fr" => "Finance"
            "en" => "Finance"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      11 => Essec\Faculty\Model\Contribution {#2278
        #_index: "academ_contributions"
        #_id: "10000"
        #_source: array:18 [
          "id" => "10000"
          "slug" => "econometrics"
          "yearMonth" => "2004-01"
          "year" => "2004"
          "title" => "Econometrics"
          "description" => "BAUWENS, L. et ROMBOUTS, J. (2004). Econometrics. Dans: <i>Handbook of Computational Statistics</i>. 1st ed. Springer, pp. 951-980."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "BAUWENS Luc"
            ]
          ]
          "ouvrage" => "Handbook of Computational Statistics"
          "keywords" => []
          "updatedAt" => "2020-12-17 18:37:46"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => "951-980"
            "volume" => null
            "number" => null
          ]
          "type" => array:2 [
            "fr" => "Chapitres"
            "en" => "Book chapters"
          ]
          "support_type" => array:2 [
            "fr" => "Editeur"
            "en" => "Publisher"
          ]
          "countries" => array:2 [
            "fr" => null
            "en" => null
          ]
          "abstract" => array:2 [
            "fr" => null
            "en" => null
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      12 => Essec\Faculty\Model\Contribution {#2279
        #_index: "academ_contributions"
        #_id: "10048"
        #_source: array:18 [
          "id" => "10048"
          "slug" => "clustered-panel-data-models-an-efficient-approach-for-nowcasting-from-poor-data"
          "yearMonth" => "2005-01"
          "year" => "2005"
          "title" => "Clustered Panel data models: An Efficient Approach for Nowcasting from Poor Data"
          "description" => "MOUCHART, M. et ROMBOUTS, J. (2005). Clustered Panel data models: An Efficient Approach for Nowcasting from Poor Data. <i>International Journal of Forecasting</i>, 21, pp. 577-594."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "MOUCHART Michel"
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2021-07-13 14:31:25"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => "577-594"
            "volume" => "21"
            "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" => null
            "en" => null
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      13 => Essec\Faculty\Model\Contribution {#2280
        #_index: "academ_contributions"
        #_id: "10141"
        #_source: array:18 [
          "id" => "10141"
          "slug" => "multivariate-garch-models-a-survey"
          "yearMonth" => "2006-01"
          "year" => "2006"
          "title" => "Multivariate GARCH Models: A Survey"
          "description" => "LAURENT, S., BAUWENS, L. et ROMBOUTS, J. (2006). Multivariate GARCH Models: A Survey. <i>Journal of Applied Econometrics</i>, 21(1), pp. 79-109."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "LAURENT Sebastien"
            ]
            2 => array:1 [
              "name" => "BAUWENS Luc"
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2021-07-13 14:31:27"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => "79-109"
            "volume" => "21"
            "number" => "1"
          ]
          "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" => null
            "en" => null
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      14 => Essec\Faculty\Model\Contribution {#2281
        #_index: "academ_contributions"
        #_id: "10167"
        #_source: array:18 [
          "id" => "10167"
          "slug" => "bayesian-clustering-of-many-garch-models"
          "yearMonth" => "2007-04"
          "year" => "2007"
          "title" => "Bayesian Clustering of Many GARCH Models"
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      15 => Essec\Faculty\Model\Contribution {#2282
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      16 => Essec\Faculty\Model\Contribution {#2283
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      17 => Essec\Faculty\Model\Contribution {#2284
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      18 => Essec\Faculty\Model\Contribution {#2285
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      19 => Essec\Faculty\Model\Contribution {#2286
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          "description" => "BOUADDI, M. et ROMBOUTS, J. (2009). Mixed Exponential Power Asymmetric Conditional Heteroskedasticity. <i>Studies in Nonlinear Dynamics and Econometrics</i>, 13(3), pp. 1-30."
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      20 => Essec\Faculty\Model\Contribution {#2287
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          "slug" => "semiparametric-multivariate-density-estimation-for-positive-data-using-copulas"
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          "title" => "Semiparametric Multivariate Density Estimation for Positive Data Using Copulas"
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      21 => Essec\Faculty\Model\Contribution {#2288
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          "title" => "Why Are AI Use Cases Not Going Live? MLOPS Bring an Answer"
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      22 => Essec\Faculty\Model\Contribution {#2289
        #_index: "academ_contributions"
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          "slug" => "the-experience-game-changer"
          "yearMonth" => "2021-03"
          "year" => "2021"
          "title" => "The Experience Game-Changer"
          "description" => "ROMBOUTS, J. (2021). The Experience Game-Changer. <i>ESSEC Knowledge</i>."
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              "name" => "ROMBOUTS Jeroen"
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      23 => Essec\Faculty\Model\Contribution {#2290
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          "title" => "Multivariate lasso-based Forecast Combinations for stock market Volatility"
          "description" => "ROMBOUTS, J., CROUX, C. et WILMS, I. (2019). Multivariate lasso-based Forecast Combinations for stock market Volatility. Dans: 2019 3rd International Conference on Econometrics and Statistics."
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              "name" => "ROMBOUTS Jeroen"
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      24 => Essec\Faculty\Model\Contribution {#2291
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      25 => Essec\Faculty\Model\Contribution {#2292
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          "title" => "The Value of Multivariate Model Sophistication: An Application to Pricing Dow Jones Industrial Average Options"
          "description" => "ROMBOUTS, J. (2013). The Value of Multivariate Model Sophistication: An Application to Pricing Dow Jones Industrial Average Options. Dans: 30th International Conference of the French Finance Association."
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      26 => Essec\Faculty\Model\Contribution {#2293
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          "title" => "Modeling Higher Moments and Risk Premiums for S&P 500 Returns"
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          "authors" => array:1 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
          ]
          "ouvrage" => "2024 Quantitative Finance and Financial Econometrics"
          "keywords" => []
          "updatedAt" => "2024-07-19 11:27:18"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => ""
            "volume" => ""
            "number" => ""
          ]
          "type" => array:2 [
            "fr" => "Communications dans une conférence"
            "en" => "Presentations at an Academic or Professional conference"
          ]
          "support_type" => array:2 [
            "fr" => null
            "en" => null
          ]
          "countries" => array:2 [
            "fr" => null
            "en" => null
          ]
          "abstract" => array:2 [
            "fr" => ""
            "en" => ""
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      27 => Essec\Faculty\Model\Contribution {#2294
        #_index: "academ_contributions"
        #_id: "10207"
        #_source: array:18 [
          "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" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      28 => Essec\Faculty\Model\Contribution {#2295
        #_index: "academ_contributions"
        #_id: "10276"
        #_source: array:18 [
          "id" => "10276"
          "slug" => "density-and-hazard-rate-estimation-for-censored-and-%ce%b1-mixing-data-using-gamma-kernels"
          "yearMonth" => "2008-10"
          "year" => "2008"
          "title" => "Density and hazard rate estimation for censored and α-mixing data using gamma kernels"
          "description" => "BOUEZMARNI, T. et ROMBOUTS, J. (2008). Density and hazard rate estimation for censored and α-mixing data using gamma kernels. <i>Journal of Nonparametric Statistics</i>, 20(7), pp. 627-643."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "BOUEZMARNI Taoufik"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:5 [
            0 => "gamma kernel"
            1 => "Kaplan Meier"
            2 => "density and hazard function"
            3 => "mean integrated squared errorconsistency"
            4 => "asymptotic normality"
          ]
          "updatedAt" => "2022-11-28 10:59:15"
          "publicationUrl" => "https://doi.org/10.1080/10485250802290670"
          "publicationInfo" => array:3 [
            "pages" => "627-643"
            "volume" => "20"
            "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" => "In this paper, we consider the non-parametric estimation for a density and hazard rate function for right censored α-mixing survival time data using kernel smoothing techniques. As survival times are positive with potentially high concentration at zero, one has to take into account the bias problems when the functions are estimated in the boundary region. In this paper, gamma kernel estimators of the density and the hazard rate function are proposed. The estimators use adaptive weights depending on the point in which we estimate the function, and they are robust to the boundary bias problem. For both estimators, the mean-squared error properties, including the rate of convergence, the almost sure consistency, and the asymptotic normality, are investigated. The results of a simulation study demonstrate the performance of the proposed estimators."
            "en" => "In this paper, we consider the non-parametric estimation for a density and hazard rate function for right censored α-mixing survival time data using kernel smoothing techniques. As survival times are positive with potentially high concentration at zero, one has to take into account the bias problems when the functions are estimated in the boundary region. In this paper, gamma kernel estimators of the density and the hazard rate function are proposed. The estimators use adaptive weights depending on the point in which we estimate the function, and they are robust to the boundary bias problem. For both estimators, the mean-squared error properties, including the rate of convergence, the almost sure consistency, and the asymptotic normality, are investigated. The results of a simulation study demonstrate the performance of the proposed estimators."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      29 => Essec\Faculty\Model\Contribution {#2296
        #_index: "academ_contributions"
        #_id: "10377"
        #_source: array:18 [
          "id" => "10377"
          "slug" => "asymptotic-properties-of-the-bernstein-density-copula-estimator-for-%ce%b1-mixing-data"
          "yearMonth" => "2010-01"
          "year" => "2010"
          "title" => "Asymptotic properties of the Bernstein density copula estimator for  α-mixing data"
          "description" => "BOUEZMARNI, T., ROMBOUTS, J. et TAAMOUTI, A. (2010). Asymptotic properties of the Bernstein density copula estimator for  α-mixing data. <i>Journal of Multivariate Analysis</i>, 101(1), pp. 1-10."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "BOUEZMARNI Taoufik"
            ]
            2 => array:1 [
              "name" => "TAAMOUTI Abderrahim"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:5 [
            0 => "Nonparametric estimation"
            1 => "Copula"
            2 => "Bernstein polynomial-mixing"
            3 => "Asymptotic properties"
            4 => "Boundary bias"
          ]
          "updatedAt" => "2022-11-28 10:56:09"
          "publicationUrl" => "https://doi.org/10.1016/j.jmva.2009.02.014"
          "publicationInfo" => array:3 [
            "pages" => "1-10"
            "volume" => "101"
            "number" => "1"
          ]
          "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" => "Copulas are extensively used for dependence modeling. In many cases the data does not reveal how the dependence can be modeled using a particular parametric copula. Nonparametric copulas do not share this problem since they are entirely data based. This paper proposes nonparametric estimation of the density copula for -mixing data using Bernstein polynomials. We focus only on the dependence structure between stochastic processes, captured by the copula density defined on the unit cube, and not the complete distribution. We study the asymptotic properties of the Bernstein density copula, i.e., we provide the exact asymptotic bias and variance, we establish the uniform strong consistency and the asymptotic normality. An empirical application is considered to illustrate the dependence structure among international stock markets (US and Canada) using the Bernstein density copula estimator."
            "en" => "Copulas are extensively used for dependence modeling. In many cases the data does not reveal how the dependence can be modeled using a particular parametric copula. Nonparametric copulas do not share this problem since they are entirely data based. This paper proposes nonparametric estimation of the density copula for -mixing data using Bernstein polynomials. We focus only on the dependence structure between stochastic processes, captured by the copula density defined on the unit cube, and not the complete distribution. We study the asymptotic properties of the Bernstein density copula, i.e., we provide the exact asymptotic bias and variance, we establish the uniform strong consistency and the asymptotic normality. An empirical application is considered to illustrate the dependence structure among international stock markets (US and Canada) using the Bernstein density copula estimator."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      30 => Essec\Faculty\Model\Contribution {#2297
        #_index: "academ_contributions"
        #_id: "10403"
        #_source: array:18 [
          "id" => "10403"
          "slug" => "nonparametric-density-estimation-for-multivariate-bounded-data"
          "yearMonth" => "2010-01"
          "year" => "2010"
          "title" => "Nonparametric Density Estimation for Multivariate Bounded Data"
          "description" => "BOUEZMARNI, T. et ROMBOUTS, J. (2010). Nonparametric Density Estimation for Multivariate Bounded Data. <i>Journal of Statistical Planning and Inference</i>, 140(1), pp. 139-152."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "BOUEZMARNI Taoufik"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:6 [
            0 => "Asymmetric kernels"
            1 => "Multivariate boundary bias"
            2 => "Nonparametric multivariate density estimation"
            3 => "Asymptotic properties"
            4 => "Bandwidth selection"
            5 => "Least squares cross-validation"
          ]
          "updatedAt" => "2021-07-13 14:31:34"
          "publicationUrl" => "https://doi.org/10.1016/j.jspi.2009.07.013"
          "publicationInfo" => array:3 [
            "pages" => "139-152"
            "volume" => "140"
            "number" => "1"
          ]
          "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 propose a new nonparametric estimator for the density function of multivariate bounded data. As frequently observed in practice, the variables may be partially bounded (e.g. nonnegative) or completely bounded (e.g. in the unit interval). In addition, the variables may have a point mass. We reduce the conditions on the underlying density to a minimum by proposing a nonparametric approach. By using a gamma, a beta, or a local linear kernel (also called boundary kernels), in a product kernel, the suggested estimator becomes simple in implementation and robust to the well known boundary bias problem. We investigate the mean integrated squared error properties, including the rate of convergence, uniform strong consistency and asymptotic normality. We establish consistency of the least squares cross-validation method to select optimal bandwidth parameters. A detailed simulation study investigates the performance of the estimators. Applications using lottery and corporate finance data are provided."
            "en" => "We propose a new nonparametric estimator for the density function of multivariate bounded data. As frequently observed in practice, the variables may be partially bounded (e.g. nonnegative) or completely bounded (e.g. in the unit interval). In addition, the variables may have a point mass. We reduce the conditions on the underlying density to a minimum by proposing a nonparametric approach. By using a gamma, a beta, or a local linear kernel (also called boundary kernels), in a product kernel, the suggested estimator becomes simple in implementation and robust to the well known boundary bias problem. We investigate the mean integrated squared error properties, including the rate of convergence, uniform strong consistency and asymptotic normality. We establish consistency of the least squares cross-validation method to select optimal bandwidth parameters. A detailed simulation study investigates the performance of the estimators. Applications using lottery and corporate finance data are provided."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      31 => Essec\Faculty\Model\Contribution {#2298
        #_index: "academ_contributions"
        #_id: "10404"
        #_source: array:18 [
          "id" => "10404"
          "slug" => "nonparametric-density-estimation-for-positive-time-series"
          "yearMonth" => "2010-02"
          "year" => "2010"
          "title" => "Nonparametric Density Estimation for Positive Time Series"
          "description" => "BOUEZMARNI, T. et ROMBOUTS, J. (2010). Nonparametric Density Estimation for Positive Time Series. <i>Computational Statistics and Data Analysis</i>, 54(2), pp. 245-261."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "BOUEZMARNI Taoufik"
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2021-07-13 14:31:34"
          "publicationUrl" => "https://doi.org/10.1016/j.csda.2009.08.016"
          "publicationInfo" => array:3 [
            "pages" => "245-261"
            "volume" => "54"
            "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" => "The Gaussian kernel density estimator is known to have substantial problems for bounded random variables with high density at the boundaries. For independent and identically distributed data, several solutions have been put forward to solve this boundary problem. In this paper, we propose the gamma kernel estimator as a density estimator for positive time series data from a stationary -mixing process. We derive the mean (integrated) squared error and asymptotic normality. In a Monte Carlo simulation, we generate data from an autoregressive conditional duration model and a stochastic volatility model. We study the local and global behavior of the estimator and we find that the gamma kernel estimator outperforms the local linear density estimator and the Gaussian kernel estimator based on log-transformed data. We also illustrate the good performance of the -block cross-validation method as a bandwidth selection procedure. An application to data from financial transaction durations and realized volatility is provided."
            "en" => "The Gaussian kernel density estimator is known to have substantial problems for bounded random variables with high density at the boundaries. For independent and identically distributed data, several solutions have been put forward to solve this boundary problem. In this paper, we propose the gamma kernel estimator as a density estimator for positive time series data from a stationary -mixing process. We derive the mean (integrated) squared error and asymptotic normality. In a Monte Carlo simulation, we generate data from an autoregressive conditional duration model and a stochastic volatility model. We study the local and global behavior of the estimator and we find that the gamma kernel estimator outperforms the local linear density estimator and the Gaussian kernel estimator based on log-transformed data. We also illustrate the good performance of the -block cross-validation method as a bandwidth selection procedure. An application to data from financial transaction durations and realized volatility is provided."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      32 => Essec\Faculty\Model\Contribution {#2299
        #_index: "academ_contributions"
        #_id: "10416"
        #_source: array:18 [
          "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" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      33 => Essec\Faculty\Model\Contribution {#2300
        #_index: "academ_contributions"
        #_id: "10427"
        #_source: array:18 [
          "id" => "10427"
          "slug" => "multivariate-option-pricing-with-time-varying-volatility-and-correlations"
          "yearMonth" => "2011-09"
          "year" => "2011"
          "title" => "Multivariate Option Pricing with Time Varying Volatility and Correlations"
          "description" => "ROMBOUTS, J. et STENTOFT, L. (2011). Multivariate Option Pricing with Time Varying Volatility and Correlations. <i>Journal of Banking & Finance</i>, 35(9), pp. 2267-2281."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "STENTOFT Lars"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:3 [
            0 => "Multivariate risk premia"
            1 => "Option pricing"
            2 => "GARCH models"
          ]
          "updatedAt" => "2021-07-13 14:31:35"
          "publicationUrl" => "https://doi.org/10.1016/j.jbankfin.2011.01.025"
          "publicationInfo" => array:3 [
            "pages" => "2267-2281"
            "volume" => "35"
            "number" => "9"
          ]
          "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" => "In this paper we consider option pricing using multivariate models for asset returns. Specifically, we demonstrate the existence of an equivalent martingale measure, we characterize the risk neutral dynamics, and we provide a feasible way for pricing options in this framework. Our application confirms the importance of allowing for dynamic correlation, and it shows that accommodating correlation risk and modeling non-Gaussian features with multivariate mixtures of normals substantially changes the estimated option prices."
            "en" => "In this paper we consider option pricing using multivariate models for asset returns. Specifically, we demonstrate the existence of an equivalent martingale measure, we characterize the risk neutral dynamics, and we provide a feasible way for pricing options in this framework. Our application confirms the importance of allowing for dynamic correlation, and it shows that accommodating correlation risk and modeling non-Gaussian features with multivariate mixtures of normals substantially changes the estimated option prices."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      34 => Essec\Faculty\Model\Contribution {#2301
        #_index: "academ_contributions"
        #_id: "10443"
        #_source: array:18 [
          "id" => "10443"
          "slug" => "nonparametric-copula-based-test-for-conditional-independence-with-applications-to-granger-causality"
          "yearMonth" => "2012-04"
          "year" => "2012"
          "title" => "Nonparametric Copula-Based Test for Conditional Independence with Applications to Granger Causality"
          "description" => "BOUEZMARNI, T., ROMBOUTS, J. et TAAMOUTI, A. (2012). Nonparametric Copula-Based Test for Conditional Independence with Applications to Granger Causality. <i>Journal of Business and Economic Statistics</i>, 30(2), pp. 275-287."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "BOUEZMARNI Taoufik"
            ]
            2 => array:1 [
              "name" => "TAAMOUTI Abderrahim"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:6 [
            0 => "Bernstein density copula"
            1 => "Bootstrap"
            2 => "Conditional independence"
            3 => "Granger noncausality"
            4 => "Nonparametric tests"
            5 => "Volatility asymmetry"
          ]
          "updatedAt" => "2022-11-28 10:52:44"
          "publicationUrl" => "https://doi.org/10.1080/07350015.2011.638831"
          "publicationInfo" => array:3 [
            "pages" => "275-287"
            "volume" => "30"
            "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" => "This article proposes a new nonparametric test for conditional independence that can directly be applied to test for Granger causality. Based on the comparison of copula densities, the test is easy to implement because it does not involve a weighting function in the test statistic, and it can be applied in general settings since there is no restriction on the dimension of the time series data. In fact, to apply the test, only a bandwidth is needed for the nonparametric copula. We prove that the test statistic is asymptotically pivotal under the null hypothesis, establishes local power properties, and motivates the validity of the bootstrap technique that we use in finite sample settings. A simulation study illustrates the size and power properties of the test. We illustrate the practical relevance of our test by considering two empirical applications where we examine the Granger noncausality between financial variables. In a first application and contrary to the general findings in the literature, we provide evidence on two alternative mechanisms of nonlinear interaction between returns and volatilities: nonlinear leverage and volatility feedback effects. This can help better understand the well known asymmetric volatility phenomenon. In a second application, we investigate the Granger causality between stock index returns and trading volume. We find convincing evidence of linear and nonlinear feedback effects from stock returns to volume, but a weak evidence of nonlinear feedback effect from volume to stock returns."
            "en" => "This article proposes a new nonparametric test for conditional independence that can directly be applied to test for Granger causality. Based on the comparison of copula densities, the test is easy to implement because it does not involve a weighting function in the test statistic, and it can be applied in general settings since there is no restriction on the dimension of the time series data. In fact, to apply the test, only a bandwidth is needed for the nonparametric copula. We prove that the test statistic is asymptotically pivotal under the null hypothesis, establishes local power properties, and motivates the validity of the bootstrap technique that we use in finite sample settings. A simulation study illustrates the size and power properties of the test. We illustrate the practical relevance of our test by considering two empirical applications where we examine the Granger noncausality between financial variables. In a first application and contrary to the general findings in the literature, we provide evidence on two alternative mechanisms of nonlinear interaction between returns and volatilities: nonlinear leverage and volatility feedback effects. This can help better understand the well known asymmetric volatility phenomenon. In a second application, we investigate the Granger causality between stock index returns and trading volume. We find convincing evidence of linear and nonlinear feedback effects from stock returns to volume, but a weak evidence of nonlinear feedback effect from volume to stock returns."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      35 => Essec\Faculty\Model\Contribution {#2302
        #_index: "academ_contributions"
        #_id: "10469"
        #_source: array:18 [
          "id" => "10469"
          "slug" => "on-marginal-likelihood-computation-in-change-point-models"
          "yearMonth" => "2012-11"
          "year" => "2012"
          "title" => "On Marginal Likelihood Computation in Change-Point Models"
          "description" => "BAUWENS, L. et ROMBOUTS, J. (2012). On Marginal Likelihood Computation in Change-Point Models. <i>Computational Statistics and Data Analysis</i>, 56(11), pp. 3415-3429."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "BAUWENS Luc"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:4 [
            0 => "BIC"
            1 => "Change-point model"
            2 => "Chib’s method"
            3 => "Marginal likelihood"
          ]
          "updatedAt" => "2021-07-13 14:31:36"
          "publicationUrl" => "https://doi.org/10.1016/j.csda.2010.06.025"
          "publicationInfo" => array:3 [
            "pages" => "3415-3429"
            "volume" => "56"
            "number" => "11"
          ]
          "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" => "Change-point models are useful for modeling time series subject to structural breaks. For interpretation and forecasting, it is essential to estimate correctly the number of change points in this class of models. In Bayesian inference, the number of change points is typically chosen by the marginal likelihood criterion, computed by Chib’s method. This method requires one to select a value in the parameter space at which the computation is performed. Bayesian inference for a change-point dynamic regression model and the computation of its marginal likelihood are explained. Motivated by results from three empirical illustrations, a simulation study shows that Chib’s method is robust with respect to the choice of the parameter value used in the computations, among posterior mean, mode and quartiles. However, taking into account the precision of the marginal likelihood estimator, the overall recommendation is to use the posterior mode or median. Furthermore, the performance of the Bayesian information criterion, which is based on maximum likelihood estimates, in selecting the correct model is comparable to that of the marginal likelihood."
            "en" => "Change-point models are useful for modeling time series subject to structural breaks. For interpretation and forecasting, it is essential to estimate correctly the number of change points in this class of models. In Bayesian inference, the number of change points is typically chosen by the marginal likelihood criterion, computed by Chib’s method. This method requires one to select a value in the parameter space at which the computation is performed. Bayesian inference for a change-point dynamic regression model and the computation of its marginal likelihood are explained. Motivated by results from three empirical illustrations, a simulation study shows that Chib’s method is robust with respect to the choice of the parameter value used in the computations, among posterior mean, mode and quartiles. However, taking into account the precision of the marginal likelihood estimator, the overall recommendation is to use the posterior mode or median. Furthermore, the performance of the Bayesian information criterion, which is based on maximum likelihood estimates, in selecting the correct model is comparable to that of the marginal likelihood."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      36 => Essec\Faculty\Model\Contribution {#2303
        #_index: "academ_contributions"
        #_id: "10810"
        #_source: array:18 [
          "id" => "10810"
          "slug" => "dynamics-of-variance-risk-premia-a-new-model-for-disentangling-the-price-of-risk"
          "yearMonth" => "2020-08"
          "year" => "2020"
          "title" => "Dynamics of variance risk premia: A new model for disentangling the price of risk"
          "description" => "ROMBOUTS, J., VIOLANTE, F. et STENTOFT, L. (2020). Dynamics of variance risk premia: A new model for disentangling the price of risk. <i>Journal of Econometrics</i>, 217(2), pp. 312-334."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "VIOLANTE Francesco"
            ]
            2 => array:1 [
              "name" => "STENTOFT Lars"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:3 [
            0 => "Variance risk premium"
            1 => "Return predictability"
            2 => "Sentiment indicators"
          ]
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => "https://doi.org/10.1016/j.jeconom.2019.12.006"
          "publicationInfo" => array:3 [
            "pages" => "312-334"
            "volume" => "217"
            "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" => "This paper formulates a new dynamic model for the variance risk premium based on a state space representation of a bivariate system for the observable ex-post realized variance and the ex-ante option implied variance expectation. A regime switching structure accommodates for periods of unusually high volatility, heterogeneous dynamics and changes in the dependence between the latent states. The model allows separating the continuous component of the variance risk premium from the impact of jumps on option implied variance expectations. Using options and high frequency returns for the S&P500 index, we explain what is generating return predictability by disentangling the part of the variance risk premium associated with normal sized price fluctuations from that associated with tail events. The latter component predicts to a significant extent, and asymmetrically with respect to their sign, future market return variations."
            "en" => "This paper formulates a new dynamic model for the variance risk premium based on a state space representation of a bivariate system for the observable ex-post realized variance and the ex-ante option implied variance expectation. A regime switching structure accommodates for periods of unusually high volatility, heterogeneous dynamics and changes in the dependence between the latent states. The model allows separating the continuous component of the variance risk premium from the impact of jumps on option implied variance expectations. Using options and high frequency returns for the S&P500 index, we explain what is generating return predictability by disentangling the part of the variance risk premium associated with normal sized price fluctuations from that associated with tail events. The latter component predicts to a significant extent, and asymmetrically with respect to their sign, future market return variations."
          ]
          "authors_fields" => array:2 [
            "fr" => "Finance"
            "en" => "Finance"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      37 => Essec\Faculty\Model\Contribution {#2304
        #_index: "academ_contributions"
        #_id: "10811"
        #_source: array:18 [
          "id" => "10811"
          "slug" => "nonlinear-financial-econometrics-joe-special-issue-introduction"
          "yearMonth" => "2020-08"
          "year" => "2020"
          "title" => "Nonlinear financial econometrics JoE special issue introduction"
          "description" => "ROMBOUTS, J., SCAILLET, O., VEREDAS, D. et ZAKOIAN, J.M. (2020). Nonlinear financial econometrics JoE special issue introduction. <i>Journal of Econometrics</i>, 27(2)."
          "authors" => array:4 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "SCAILLET O."
            ]
            2 => array:1 [
              "name" => "VEREDAS D."
            ]
            3 => array:1 [
              "name" => "ZAKOIAN J-M."
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2023-06-27 12:39:26"
          "publicationUrl" => "https://doi.org/10.1016/j.jeconom.2019.12.001"
          "publicationInfo" => array:3 [
            "pages" => ""
            "volume" => "27"
            "number" => "2"
          ]
          "type" => array:2 [
            "fr" => "Editeur invité d'un numéro spécial"
            "en" => "Guest editor of a journal special issue"
          ]
          "support_type" => array:2 [
            "fr" => "Revue scientifique"
            "en" => "Scientific journal"
          ]
          "countries" => array:2 [
            "fr" => null
            "en" => null
          ]
          "abstract" => array:2 [
            "fr" => ""
            "en" => ""
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      38 => Essec\Faculty\Model\Contribution {#2305
        #_index: "academ_contributions"
        #_id: "10812"
        #_source: array:18 [
          "id" => "10812"
          "slug" => "relevant-parameter-changes-in-structural-break-models"
          "yearMonth" => "2020-07"
          "year" => "2020"
          "title" => "Relevant parameter changes in structural break models"
          "description" => "DUFAYS, A. et ROMBOUTS, J. (2020). Relevant parameter changes in structural break models. <i>Journal of Econometrics</i>, 217(1), pp. 46-78."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "DUFAYS Arnaud"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:4 [
            0 => "Shrinkage prior"
            1 => "Structural break model"
            2 => "Relevant parameter change"
            3 => "Bayesian inference"
          ]
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => "https://doi.org/10.1016/j.jeconom.2019.10.008"
          "publicationInfo" => array:3 [
            "pages" => "46-78"
            "volume" => "217"
            "number" => "1"
          ]
          "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" => "Structural break time series models, which are commonly used in macroeconomics and finance, capture unknown structural changes by allowing for abrupt changes to model parameters. However, many specifications suffer from an over-parametrization issue, since typically all parameters have to change when a break occurs. We introduce a sparse change-point model to detect which parameters change over time. We propose a shrinkage prior distribution, which controls model parsimony by limiting the number of parameters that change from one structural break to another. We develop a Bayesian sampler for inference on the sparse change-point model. An extensive simulation study based on AR, ARMA and GARCH processes highlights the excellent performance of the sampler. We provide several empirical applications including an out-of-sample forecasting exercise showing that the Sparse change-point framework compares favorably with other recent time-varying parameter processes."
            "en" => "Structural break time series models, which are commonly used in macroeconomics and finance, capture unknown structural changes by allowing for abrupt changes to model parameters. However, many specifications suffer from an over-parametrization issue, since typically all parameters have to change when a break occurs. We introduce a sparse change-point model to detect which parameters change over time. We propose a shrinkage prior distribution, which controls model parsimony by limiting the number of parameters that change from one structural break to another. We develop a Bayesian sampler for inference on the sparse change-point model. An extensive simulation study based on AR, ARMA and GARCH processes highlights the excellent performance of the sampler. We provide several empirical applications including an out-of-sample forecasting exercise showing that the Sparse change-point framework compares favorably with other recent time-varying parameter processes."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      39 => Essec\Faculty\Model\Contribution {#2306
        #_index: "academ_contributions"
        #_id: "10814"
        #_source: array:18 [
          "id" => "10814"
          "slug" => "pricing-individual-stock-options-using-both-stock-and-market-index-information"
          "yearMonth" => "2020-02"
          "year" => "2020"
          "title" => "Pricing Individual Stock Options Using both Stock and Market Index Information"
          "description" => "ROMBOUTS, J., VIOLANTE, F. et STENTOFT, L. (2020). Pricing Individual Stock Options Using both Stock and Market Index Information. <i>Journal of Banking & Finance</i>, 111."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "VIOLANTE Francesco"
            ]
            2 => array:1 [
              "name" => "STENTOFT L."
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:4 [
            0 => "American option pricing"
            1 => "Economic loss"
            2 => "Forecasting"
            3 => "Multivariate GARCH"
          ]
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => "https://doi.org/10.1016/j.jbankfin.2019.1057277"
          "publicationInfo" => array:3 [
            "pages" => null
            "volume" => "111"
            "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" => "When it comes to individual stock option pricing, most applications consider a univariate framework. From a theoretical point of view this is unsatisfactory as we know that the expected return of any asset is closely related to the exposure to the market risk factors. To address this, we model the evolution of the individual stock returns together with the market index returns in a flexible bivariate model in line with theory. The model parameters are estimated using both historical returns and aggregated option data from the index and the individual stocks. We assess the model performance by pricing a large set of individual stock options on 26 major US stocks over a long time period including the global financial crisis. Our results show that the losses from using a univariate formulation amounts to 11% on average when compared to our preferred bivariate specification."
            "en" => "When it comes to individual stock option pricing, most applications consider a univariate framework. From a theoretical point of view this is unsatisfactory as we know that the expected return of any asset is closely related to the exposure to the market risk factors. To address this, we model the evolution of the individual stock returns together with the market index returns in a flexible bivariate model in line with theory. The model parameters are estimated using both historical returns and aggregated option data from the index and the individual stocks. We assess the model performance by pricing a large set of individual stock options on 26 major US stocks over a long time period including the global financial crisis. Our results show that the losses from using a univariate formulation amounts to 11% on average when compared to our preferred bivariate specification."
          ]
          "authors_fields" => array:2 [
            "fr" => "Finance"
            "en" => "Finance"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      40 => Essec\Faculty\Model\Contribution {#2307
        #_index: "academ_contributions"
        #_id: "10836"
        #_source: array:18 [
          "id" => "10836"
          "slug" => "variance-swap-payoffs-risk-premia-and-extreme-market-conditions"
          "yearMonth" => "2020-01"
          "year" => "2020"
          "title" => "Variance swap payoffs, risk premia and extreme market conditions"
          "description" => "ROMBOUTS, J., STENTOFT, L. et VIOLANTE, F. (2020). Variance swap payoffs, risk premia and extreme market conditions. <i>Econometrics and Statistics</i>, 13, pp. 106-124."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "STENTOFT L."
            ]
            2 => array:1 [
              "name" => "VIOLANTE Francesco"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:6 [
            0 => "Variance risk premium"
            1 => "Variance swaps"
            2 => "Return predictability"
            3 => "Factor model"
            4 => "Kalman filter"
            5 => "CAPM"
          ]
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => "https://doi.org/10.1016/j.ecosta.2019.05.003"
          "publicationInfo" => array:3 [
            "pages" => "106-124"
            "volume" => "13"
            "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" => "The variance risk premium (VRP) is estimated directly from synthetic variance swap payoffs. Since variance swap payoffs are highly volatile, the VRP is extracted by using signal extraction techniques based on a state-space representation of the model in combination with a simple economic constraint. The proposed approach, only requiring option implied volatilities and daily returns for the underlying asset, provides measurement error free estimates of the part of the VRP related to normal market conditions, and allows constructing variables indicating agents’ expectations under extreme market conditions. The latter variables and the VRP generate different return predictability on the major US indices. A factor model is proposed to extract a market VRP which turns out to be priced when considering Fama and French portfolios."
            "en" => "The variance risk premium (VRP) is estimated directly from synthetic variance swap payoffs. Since variance swap payoffs are highly volatile, the VRP is extracted by using signal extraction techniques based on a state-space representation of the model in combination with a simple economic constraint. The proposed approach, only requiring option implied volatilities and daily returns for the underlying asset, provides measurement error free estimates of the part of the VRP related to normal market conditions, and allows constructing variables indicating agents’ expectations under extreme market conditions. The latter variables and the VRP generate different return predictability on the major US indices. A factor model is proposed to extract a market VRP which turns out to be priced when considering Fama and French portfolios."
          ]
          "authors_fields" => array:2 [
            "fr" => "Finance"
            "en" => "Finance"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      41 => Essec\Faculty\Model\Contribution {#2308
        #_index: "academ_contributions"
        #_id: "11354"
        #_source: array:18 [
          "id" => "11354"
          "slug" => "on-the-forecasting-accuracy-of-multivariate-garch-models"
          "yearMonth" => "2012-10"
          "year" => "2012"
          "title" => "On the forecasting accuracy of multivariate GARCH models"
          "description" => "LAURENT, S., ROMBOUTS, J. et VIOLANTE, F. (2012). On the forecasting accuracy of multivariate GARCH models. <i>Journal of Applied Econometrics</i>, 27(6), pp. 934-955."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "LAURENT Sebastien"
            ]
            2 => array:1 [
              "name" => "VIOLANTE Francesco"
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2022-11-28 10:54:00"
          "publicationUrl" => "https://doi.org/10.1002/jae.1248"
          "publicationInfo" => array:3 [
            "pages" => "934-955"
            "volume" => "27"
            "number" => "6"
          ]
          "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" => "This paper addresses the question of the selection of multivariate generalized autoregressive conditional heteroskedastic (GARCH) models in terms of variance matrix forecasting accuracy, with a particular focus on relatively large‐scale problems."
            "en" => "This paper addresses the question of the selection of multivariate generalized autoregressive conditional heteroskedastic (GARCH) models in terms of variance matrix forecasting accuracy, with a particular focus on relatively large‐scale problems."
          ]
          "authors_fields" => array:2 [
            "fr" => "Finance"
            "en" => "Finance"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      42 => Essec\Faculty\Model\Contribution {#2309
        #_index: "academ_contributions"
        #_id: "11383"
        #_source: array:18 [
          "id" => "11383"
          "slug" => "multivariate-volatility-forecasts-for-stock-market-indices"
          "yearMonth" => "2021-04"
          "year" => "2021"
          "title" => "Multivariate volatility forecasts for stock market indices"
          "description" => "WILMS, I., ROMBOUTS, J. et CROUX, C. (2021). Multivariate volatility forecasts for stock market indices. <i>International Journal of Forecasting</i>, 37(2), pp. 484-499."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "WILMS Ines"
            ]
            2 => array:1 [
              "name" => "CROUX Christophe"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:5 [
            0 => "International stock markets"
            1 => "Lasso"
            2 => "Option-implied variance"
            3 => "Realized variance"
            4 => "Volatility spillover"
          ]
          "updatedAt" => "2021-09-24 10:33:27"
          "publicationUrl" => "https://doi.org/10.1016/j.ijforecast.2020.06.012"
          "publicationInfo" => array:3 [
            "pages" => "484-499"
            "volume" => "37"
            "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" => "Volatility forecasts aim to measure future risk and they are key inputs for financial analysis. In this study, we forecast the realized variance as an observable measure of volatility for several major international stock market indices and accounted for the different predictive information present in jump, continuous, and option-implied variance components. We allowed for volatility spillovers in different stock markets by using a multivariate modeling approach. We used heterogeneous autoregressive (HAR)-type models to obtain the forecasts. Based an out-of-sample forecast study, we show that: (i) including option-implied variances in the HAR model substantially improves the forecast accuracy, (ii) lasso-based lag selection methods do not outperform the parsimonious day-week-month lag structure of the HAR model, and (iii) cross-market spillover effects embedded in the multivariate HAR model have long-term forecasting power."
            "en" => "Volatility forecasts aim to measure future risk and they are key inputs for financial analysis. In this study, we forecast the realized variance as an observable measure of volatility for several major international stock market indices and accounted for the different predictive information present in jump, continuous, and option-implied variance components. We allowed for volatility spillovers in different stock markets by using a multivariate modeling approach. We used heterogeneous autoregressive (HAR)-type models to obtain the forecasts. Based an out-of-sample forecast study, we show that: (i) including option-implied variances in the HAR model substantially improves the forecast accuracy, (ii) lasso-based lag selection methods do not outperform the parsimonious day-week-month lag structure of the HAR model, and (iii) cross-market spillover effects embedded in the multivariate HAR model have long-term forecasting power."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      43 => Essec\Faculty\Model\Contribution {#2310
        #_index: "academ_contributions"
        #_id: "12760"
        #_source: array:18 [
          "id" => "12760"
          "slug" => "sparse-change%e2%80%90point-var-models"
          "yearMonth" => "2021-09"
          "year" => "2021"
          "title" => "Sparse change‐point VAR models"
          "description" => "DUFAYS, A., LI, Z., ROMBOUTS, J. et SONG, Y. (2021). Sparse change‐point VAR models. <i>Journal of Applied Econometrics</i>, 36(6), pp. 703-727."
          "authors" => array:4 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "DUFAYS Arnaud"
            ]
            2 => array:1 [
              "name" => "LI Zhuo"
            ]
            3 => array:1 [
              "name" => "SONG Yong"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:1 [
            0 => "VAR models"
          ]
          "updatedAt" => "2023-05-31 10:52:44"
          "publicationUrl" => "https://doi.org/10.1002/jae.2844"
          "publicationInfo" => array:3 [
            "pages" => "703-727"
            "volume" => "36"
            "number" => "6"
          ]
          "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" => "Change-point (CP) VAR models face a dimensionality curse due to the proliferation of parameters that arises when new breaks are detected. We introduce the Sparse CP-VAR model which determines which parameters truly vary when a break is detected. By doing so, the number of new parameters to be estimated at each regime is drastically reduced and the break dynamics becomes easier to be interpreted. The Sparse CP-VAR model disentangles the dynamics of the mean parameters and the covariance matrix. The former uses CP dynamics with shrinkage prior distributions, while the latter is driven by an infinite hidden Markov framework. An extensive simulation study is carried out to compare our approach with existing ones. We provide applications to financial and macroeconomic systems."
            "en" => "Change-point (CP) VAR models face a dimensionality curse due to the proliferation of parameters that arises when new breaks are detected. We introduce the Sparse CP-VAR model which determines which parameters truly vary when a break is detected. By doing so, the number of new parameters to be estimated at each regime is drastically reduced and the break dynamics becomes easier to be interpreted. The Sparse CP-VAR model disentangles the dynamics of the mean parameters and the covariance matrix. The former uses CP dynamics with shrinkage prior distributions, while the latter is driven by an infinite hidden Markov framework. An extensive simulation study is carried out to compare our approach with existing ones. We provide applications to financial and macroeconomic systems."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      44 => Essec\Faculty\Model\Contribution {#2311
        #_index: "academ_contributions"
        #_id: "14030"
        #_source: array:18 [
          "id" => "14030"
          "slug" => "factor-dynamics-risk-premia-and-higher-moments-in-multi-factor-option-pricing-models"
          "yearMonth" => "2022-12"
          "year" => "2022"
          "title" => "Factor Dynamics, Risk Premia, and Higher Moments in Multi-Factor Option Pricing Models"
          "description" => "DUFAYS, A., JACOBS, D. et ROMBOUTS, J. (2022). Factor Dynamics, Risk Premia, and Higher Moments in Multi-Factor Option Pricing Models. Dans: 2022  International Conference on Computational and Financial Econometrics. London."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "DUFAYS Arnaud"
            ]
            2 => array:1 [
              "name" => "JACOBS D"
            ]
          ]
          "ouvrage" => "2022  International Conference on Computational and Financial Econometrics"
          "keywords" => []
          "updatedAt" => "2023-11-29 16:37:22"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => ""
            "volume" => ""
            "number" => ""
          ]
          "type" => array:2 [
            "fr" => "Communications dans une conférence"
            "en" => "Presentations at an Academic or Professional conference"
          ]
          "support_type" => array:2 [
            "fr" => null
            "en" => null
          ]
          "countries" => array:2 [
            "fr" => null
            "en" => null
          ]
          "abstract" => array:2 [
            "fr" => ""
            "en" => ""
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      45 => Essec\Faculty\Model\Contribution {#2312
        #_index: "academ_contributions"
        #_id: "14091"
        #_source: array:18 [
          "id" => "14091"
          "slug" => "fast-filtering-with-large-option-panels-implications-for-asset-pricing"
          "yearMonth" => "2023-06"
          "year" => "2023"
          "title" => "Fast Filtering with Large Option Panels: Implications for Asset Pricing"
          "description" => "DUFAYS, A., JACOBS, K., LIU, Y. et ROMBOUTS, J. (2023). Fast Filtering with Large Option Panels: Implications for Asset Pricing. <i>Journal of Financial and Quantitative Analysis</i>, In press, pp. 1-56."
          "authors" => array:4 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "DUFAYS Arnaud"
            ]
            2 => array:1 [
              "name" => "JACOBS Kris"
            ]
            3 => array:1 [
              "name" => "LIU Yuguo"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:5 [
            0 => "Option Valuation"
            1 => "Particle MCMC"
            2 => "Posterior Density"
            3 => "Option Panels"
            4 => "Risk Premia"
          ]
          "updatedAt" => "2023-06-28 16:47:11"
          "publicationUrl" => "https://doi.org/10.1017/S0022109023000753"
          "publicationInfo" => array:3 [
            "pages" => "1-56"
            "volume" => "In press"
            "number" => ""
          ]
          "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" => "The cross-section of options holds great promise for identifying return distributions and risk premia, but estimating dynamic option valuation models with latent state variables is challenging when using large option panels. We propose a particle MCMC framework with a novel filtering approach and illustrate our method by estimating workhorse index option pricing models. Estimates of the variance risk premium, variance mean reversion, and higher moments differ from the literature. We show that these differences are due to the composition of the option sample. Restrictions on the option sample's maturity dimension have the strongest impact on parameter inference and option fit in these models."
            "en" => "The cross-section of options holds great promise for identifying return distributions and risk premia, but estimating dynamic option valuation models with latent state variables is challenging when using large option panels. We propose a particle MCMC framework with a novel filtering approach and illustrate our method by estimating workhorse index option pricing models. Estimates of the variance risk premium, variance mean reversion, and higher moments differ from the literature. We show that these differences are due to the composition of the option sample. Restrictions on the option sample's maturity dimension have the strongest impact on parameter inference and option fit in these models."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      46 => Essec\Faculty\Model\Contribution {#2313
        #_index: "academ_contributions"
        #_id: "14480"
        #_source: array:18 [
          "id" => "14480"
          "slug" => "tuning-in-what-ai-and-user-generated-content-can-tell-us-about-consumers"
          "yearMonth" => "2023-07"
          "year" => "2023"
          "title" => "Tuning In - What AI and User Generated Content Can Tell Us About Consumers"
          "description" => "KÜBLER, R. et ROMBOUTS, J. (2023). Tuning In - What AI and User Generated Content Can Tell Us About Consumers. <i>ESSEC Knowledge</i>."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "KÜBLER Raoul"
              "bid" => "B00806952"
              "slug" => "kubler-raoul"
            ]
            1 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
          ]
          "ouvrage" => ""
          "keywords" => []
          "updatedAt" => "2024-10-31 13:51:19"
          "publicationUrl" => "https://knowledge.essec.edu/en/innovation/tuning-ai-and-user-generated-content-consumers.html"
          "publicationInfo" => array:3 [
            "pages" => null
            "volume" => null
            "number" => null
          ]
          "type" => array:2 [
            "fr" => "Articles ou vidéos de vulgarisation"
            "en" => "Press article, video or other popular media"
          ]
          "support_type" => array:2 [
            "fr" => "Presse"
            "en" => "Press"
          ]
          "countries" => array:2 [
            "fr" => null
            "en" => null
          ]
          "abstract" => array:2 [
            "fr" => "We are living in the most data-rich period of humankind. We create and store more data on a daily basis than our ancestors did in the last 2000 years - combined. This data comes from various digital sources, where consumers leave intentionally (and unintentionally) valuable information for marketers in the form of user-generated content.  The authors explain what this data can tell us about consumer behavior."
            "en" => "We are living in the most data-rich period of humankind. We create and store more data on a daily basis than our ancestors did in the last 2000 years - combined. This data comes from various digital sources, where consumers leave intentionally (and unintentionally) valuable information for marketers in the form of user-generated content.  The authors explain what this data can tell us about consumer behavior."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      47 => Essec\Faculty\Model\Contribution {#2314
        #_index: "academ_contributions"
        #_id: "14673"
        #_source: array:18 [
          "id" => "14673"
          "slug" => "monitoring-machine-learning-forecasts-for-platform-data-streams"
          "yearMonth" => "2024-01"
          "year" => "2024"
          "title" => "Monitoring Machine Learning Forecasts for Platform Data Streams"
          "description" => "ROMBOUTS, J. et WILMS, I. (2024). Monitoring Machine Learning Forecasts for Platform Data Streams. Dans: 6th Institute for Mathematical Statistics – Asia-Pacific Rim Meeting (IMS-APRM 2024). Melbourne."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "WILMS Ines "
            ]
          ]
          "ouvrage" => "6th Institute for Mathematical Statistics – Asia-Pacific Rim Meeting (IMS-APRM 2024)"
          "keywords" => []
          "updatedAt" => "2024-01-31 01:00:38"
          "publicationUrl" => null
          "publicationInfo" => array:3 [
            "pages" => ""
            "volume" => ""
            "number" => ""
          ]
          "type" => array:2 [
            "fr" => "Communications dans une conférence"
            "en" => "Presentations at an Academic or Professional conference"
          ]
          "support_type" => array:2 [
            "fr" => null
            "en" => null
          ]
          "countries" => array:2 [
            "fr" => null
            "en" => null
          ]
          "abstract" => array:2 [
            "fr" => ""
            "en" => ""
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      48 => Essec\Faculty\Model\Contribution {#2315
        #_index: "academ_contributions"
        #_id: "14790"
        #_source: array:18 [
          "id" => "14790"
          "slug" => "ai-as-perceived-by-essec-students-a-response-to-contemporary-issues"
          "yearMonth" => "2023-11"
          "year" => "2023"
          "title" => "AI as Perceived by ESSEC Students: A Response to Contemporary Issues"
          "description" => "HUBER, T. et ROMBOUTS, J. (2023). AI as Perceived by ESSEC Students: A Response to Contemporary Issues. <i>ESSEC Knowledge</i>."
          "authors" => array:2 [
            0 => array:3 [
              "name" => "HUBER Thomas"
              "bid" => "B00759533"
              "slug" => "huber-thomas"
            ]
            1 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:3 [
            0 => "AI"
            1 => "artificial intelligence"
            2 => "innovation"
          ]
          "updatedAt" => "2024-05-15 10:56:59"
          "publicationUrl" => "https://knowledge.essec.edu/en/innovation/ai-as-perceived-by-essec-students.html"
          "publicationInfo" => array:3 [
            "pages" => ""
            "volume" => ""
            "number" => ""
          ]
          "type" => array:2 [
            "fr" => "Articles ou vidéos de vulgarisation"
            "en" => "Press article, video or other popular media"
          ]
          "support_type" => array:2 [
            "fr" => "Presse"
            "en" => "Press"
          ]
          "countries" => array:2 [
            "fr" => null
            "en" => null
          ]
          "abstract" => array:2 [
            "fr" => "ESSEC students envision a myriad of managerial and business applications for emerging AI technologies."
            "en" => "ESSEC students envision a myriad of managerial and business applications for emerging AI technologies."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      49 => Essec\Faculty\Model\Contribution {#2316
        #_index: "academ_contributions"
        #_id: "14844"
        #_source: array:18 [
          "id" => "14844"
          "slug" => "cross-temporal-forecast-reconciliation-at-digital-platforms-with-machine-learning"
          "yearMonth" => "2024-06"
          "year" => "2024"
          "title" => "Cross-temporal forecast reconciliation at digital platforms with machine learning"
          "description" => "ROMBOUTS, J., TERNES, M. et WILMS, I. (2024). Cross-temporal forecast reconciliation at digital platforms with machine learning. <i>International Journal of Forecasting</i>, In press."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "Ternes Marie"
            ]
            2 => array:1 [
              "name" => "Wilms Ines"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:5 [
            0 => "Hierarchical time series"
            1 => "Forecast reconciliation"
            2 => "Machine learning"
            3 => "Cross-temporal aggregation"
            4 => "Demand forecasting"
          ]
          "updatedAt" => "2024-10-31 13:51:19"
          "publicationUrl" => "https://doi.org/10.1016/j.ijforecast.2024.05.008"
          "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" => "Platform businesses operate on a digital core, and their decision-making requires high-dimensional accurate forecast streams at different levels of cross-sectional (e.g., geographical regions) and temporal aggregation (e.g., minutes to days). It also necessitates coherent forecasts across all hierarchy levels to ensure aligned decision-making across different planning units such as pricing, product, controlling, and strategy. Given that platform data streams feature complex characteristics and interdependencies, we introduce a non-linear hierarchical forecast reconciliation method that produces crosstemporal reconciled forecasts in a direct and automated way through popular machine learning methods. The method is sufficiently fast to allow forecast-based high-frequency decision-making that platforms require. We empirically test our framework on unique, large-scale streaming datasets from a leading on-demand delivery platform in Europe and a bicycle-sharing system in New York City."
            "en" => "Platform businesses operate on a digital core, and their decision-making requires high-dimensional accurate forecast streams at different levels of cross-sectional (e.g., geographical regions) and temporal aggregation (e.g., minutes to days). It also necessitates coherent forecasts across all hierarchy levels to ensure aligned decision-making across different planning units such as pricing, product, controlling, and strategy. Given that platform data streams feature complex characteristics and interdependencies, we introduce a non-linear hierarchical forecast reconciliation method that produces crosstemporal reconciled forecasts in a direct and automated way through popular machine learning methods. The method is sufficiently fast to allow forecast-based high-frequency decision-making that platforms require. We empirically test our framework on unique, large-scale streaming datasets from a leading on-demand delivery platform in Europe and a bicycle-sharing system in New York City."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
      50 => Essec\Faculty\Model\Contribution {#2317
        #_index: "academ_contributions"
        #_id: "14855"
        #_source: array:18 [
          "id" => "14855"
          "slug" => "fast-forecasting-of-unstable-data-streams-for-on-demand-service-platforms"
          "yearMonth" => "2024-05"
          "year" => "2024"
          "title" => "Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms"
          "description" => "HU, Y.J., ROMBOUTS, J. et WILMS, I. (2024). Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms. <i>Information Systems Research</i>, In press, pp. 1-20."
          "authors" => array:3 [
            0 => array:3 [
              "name" => "ROMBOUTS Jeroen"
              "bid" => "B00469813"
              "slug" => "rombouts-jeroen"
            ]
            1 => array:1 [
              "name" => "Hu Yu Jeffrey"
            ]
            2 => array:1 [
              "name" => "Wilms Ines"
            ]
          ]
          "ouvrage" => ""
          "keywords" => array:4 [
            0 => "e-commerce"
            1 => "platform econometrics"
            2 => "streaming data"
            3 => "forecast breakdown"
          ]
          "updatedAt" => "2024-10-31 13:51:19"
          "publicationUrl" => "https://doi.org/10.1287/isre.2023.0130"
          "publicationInfo" => array:3 [
            "pages" => "1-20"
            "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" => "On-demand service platforms face a challenging problem of forecasting a large collection of high-frequency regional demand data streams that exhibit instabilities. This paper develops a novel forecast framework that is fast and scalable and automatically assesses changing environments without human intervention. We empirically test our framework on a large-scale demand data set from a leading on-demand delivery platform in Europe and find strong performance gains from using our framework against several industry benchmarks across all geographical regions, loss functions, and both pre- and post-COVID periods. We translate forecast gains to economic impacts for this on-demand service platform by computing financial gains and reductions in computing costs."
            "en" => "On-demand service platforms face a challenging problem of forecasting a large collection of high-frequency regional demand data streams that exhibit instabilities. This paper develops a novel forecast framework that is fast and scalable and automatically assesses changing environments without human intervention. We empirically test our framework on a large-scale demand data set from a leading on-demand delivery platform in Europe and find strong performance gains from using our framework against several industry benchmarks across all geographical regions, loss functions, and both pre- and post-COVID periods. We translate forecast gains to economic impacts for this on-demand service platform by computing financial gains and reductions in computing costs."
          ]
          "authors_fields" => array:2 [
            "fr" => "Systèmes d'Information, Data Analytics et Opérations"
            "en" => "Information Systems, Data Analytics and Operations"
          ]
          "indexedAt" => "2024-12-30T14:21:51.000Z"
        ]
        +lang: "fr"
        +"_type": "_doc"
        +"_score": 5.385655
        +"parent": null
      }
    ]
    "avatar" => "https://faculty.essec.edu/wp-content/uploads/avatars/B00469813.jpg"
    "contributionCounts" => 51
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