Essec\Faculty\Model\Contribution {#2216
#_index: "academ_contributions"
#_id: "2025"
#_source: array:26 [
"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" => "2024-12-21T16:21:43.000Z"
"docTitle" => "Marginal Likelihood for Markov-switching and Change-Point GARCH Models"
"docSurtitle" => "Journal articles"
"authorNames" => "<a href="/cv/rombouts-jeroen">ROMBOUTS Jeroen</a>, BAUWENS L., DUFAYS Arnaud"
"docDescription" => "<span class="document-property-authors">ROMBOUTS Jeroen, BAUWENS L., DUFAYS Arnaud</span><br><span class="document-property-authors_fields">Information Systems, Data Analytics and Operations</span> | <span class="document-property-year">2014</span>"
"keywordList" => "<a href="#">Bayesian inference</a>, <a href="#">Simulation</a>, <a href="#">GARCH</a>, <a href="#">Markov-switching model</a>, <a href="#">Change-point model</a>, <a href="#">Marginal likelihood</a>, <a href="#">Particle MCMC</a>"
"docPreview" => "<b>Marginal Likelihood for Markov-switching and Change-Point GARCH Models</b><br><span>2014-01 | Journal articles </span>"
"docType" => "research"
"publicationLink" => "<a href="https://www.sciencedirect.com/science/article/abs/pii/S030440761300167X" target="_blank">Marginal Likelihood for Markov-switching and Change-Point GARCH Models</a>"
]
+lang: "en"
+"_type": "_doc"
+"_score": 8.755391
+"parent": null
}