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Ardia, David

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Ardia, David
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https://libra.unine.ch/handle/123456789/832

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  • Publication
    Accès libre
    Density prediction of stock index returns using GARCH models: Frequentist or Bayesian estimation?
    (2012)
    Hoogerheide, Lennart
    ;
    Ardia, David 
    ;
    Corré, Nienke
    Using GARCH models for density prediction of stock index returns, a comparison is provided between frequentist and Bayesian estimation. No significant difference is found between qualities of whole density forecasts, whereas the Bayesian approach exhibits significantly better left-tail forecast accuracy.
  • Publication
    Accès libre
    Bayesian estimation of the GARCH(1,1) model with Student-t innovations in R
    (2010)
    Ardia, David 
    ;
    Hoogerheide, Lennart
    This paper presents the R package bayesGARCH which provides functions for the Bayesian estimation of the parsimonious but effective GARCH(1,1) model with Student-t innovations. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling algorithm. The usage of the package is shown in an empirical application to exchange rate log-returns.
  • Publication
    Accès libre
    Adaptive mixture of Student-t distributions as a flexible distribution for efficient simulation: The R package AdMit
    (2009)
    Ardia, David 
    ;
    Hoogerheide, Lennart
    ;
    Van Dijk, Herman
    This paper presents the R package AdMit which provides flexible functions to approximate a certain target distribution and to efficiently generate a sample of random draws from it, given only a kernel of the target density function. The core algorithm consists of the function AdMit which fits an adaptive mixture of Student-t distributions to the density of interest. Then, importance sampling or the independence chain Metropolis-Hastings algorithm is used to obtain quantities of interest for the target density, using the fitted mixture as the importance or candidate density. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling algorithm. The relevance of the package is shown in two examples. The first aims at illustrating in detail the use of the functions provided by the package in a bivariate bimodal distribution. The second shows the relevance of the adaptive mixture procedure through the Bayesian estimation of a mixture of ARCH model fitted to foreign exchange log-returns data. The methodology is compared to standard cases of importance sampling and the Metropolis-Hastings algorithm using a naive candidate and with the Griddy-Gibbs approach.
  • Publication
    Accès libre
    AdMit: Adaptive mixtures of Student-t distributions
    (2009)
    Ardia, David 
    ;
    Hoogerheide, Lennart
    ;
    Van Dijk, Herman
    This short note presents the R package AdMit which provides flexible functions to approximate a certain target distribution and it provides an efficient sample of random draws from it, given only a kernel of the target density function. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling algorithm. To illustrate the use of the package, we apply the AdMit methodology to a bivariate bimodal distribution. We describe the use of the functions provided by the package and document the ability and relevance of the methodology to reproduce the shape of non-elliptical distributions.