Bayesian clustering of many GARCH models
AbstractWe consider the estimation of a large number of GARCH models, of the order of several hundreds. To achieve parsimony, we classify the series in a small number of groups. Within a cluster, the series share the same model and the same parameters. Each cluster contains therefore similar series. We do not know a priori which series belongs to which cluster. The model is a finite mixture of distributions, where the component weights are unknown parameters and each component distribution has its own conditional mean and variance. Inference is done by the Bayesian approach, using data augmentation techniques. Illustrations are provided.
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Bibliographic InfoPaper provided by Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) in its series CORE Discussion Papers with number 2003087.
Date of creation: 00 Dec 2003
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Bayesian inference; clustering; GARCH; Gibbs sampling; mixtures;
Other versions of this item:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
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- BAUWENS, Luc & LAURENT, Sébastien & ROMBOUTS, Jeroen VK, .
"Multivariate GARCH models: a survey,"
CORE Discussion Papers RP
-1847, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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