Bayesian inference for latent factor GARCH models
Latent factor GARCH models are difficult to estimate using Bayesian methods because standard Markov chain Monte Carlo samplers produce slowly mixing and inefficient draws from the posterior distributions of the model parameters. This paper describes how to apply the particle Gibbs algorithm to estim...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
05.07.2015
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Subjects | |
Online Access | Get full text |
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Summary: | Latent factor GARCH models are difficult to estimate using Bayesian methods
because standard Markov chain Monte Carlo samplers produce slowly mixing and
inefficient draws from the posterior distributions of the model parameters.
This paper describes how to apply the particle Gibbs algorithm to estimate
factor GARCH models efficiently. The method has two advantages over previous
approaches. First, it generalises in a straightfoward way to models with
multiple factors and to various members of the GARCH family. Second, it scales
up well as the dimension of the o, bservation vector increases. |
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DOI: | 10.48550/arxiv.1507.01179 |