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bayesGARCH: Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations in R

David Ardia; Lennart F. Hoogerheide

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  <identifier identifierType="DOI">10.5281/zenodo.231327</identifier>
      <creatorName>David Ardia</creatorName>
      <affiliation>University of Neuchâtel</affiliation>
      <creatorName>Lennart F. Hoogerheide</creatorName>
      <affiliation>Vrije Universiteit Amsterdam</affiliation>
    <title>bayesGARCH: Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations in R</title>
    <subject>R software</subject>
    <date dateType="Issued">2017-01-05</date>
  <resourceType resourceTypeGeneral="Software"/>
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    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;The package bayesGARCH implements in R (R Core Team, 2016) the Bayesian estimation procedure described in Ardia (2008, chapter 5) for the GARCH(1,1) model with Student-t innovations. The approach consists of a Metropolis-Hastings (MH) algorithm where the proposal distributions are constructed from auxiliary ARMA processes on the squared observations. This methodology avoids the time-consuming and difficult task, especially for non-experts, of choosing and tuning a sampling algorithm. We refer the user to Ardia (2008) and Ardia and Hoogerheide (2010) for illustrations. The latest version of the package is available at;/p&gt;</description>
    <description descriptionType="Other">{"references": ["David Ardia. Financial Risk Management with Bayesian Estimation of GARCH Models: Theory and Applications, volume 612 of Lecture Notes in Economics and Mathematical Systems. Springer-Verlag, Berlin, Germany, 2008. doi: 10.1007/978-3-540-78657-3", "David Ardia and Lennart F. Hoogerheide. Bayesian estimation of the GARCH(1,1) model with Student-t innovations. The R Journal, 2(2):41-47, 2010. URL"]}</description>
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