Bayesian Inference Methods for Univariate and Multivariate GARCH Models: A Survey
By Audronė Virbickaitė, M. C. Aus'in, Pedro Galeano. 2014
49 citations recorded by the source corpus.
This survey reviews the existing literature on the most relevant Bayesian inference methods for univariate and multivariate GARCH models. The advantages and drawbacks of each procedure are outlined as well as the advantages of the Bayesian approach versus classical procedures. The paper makes emphasis on recent Bayesian non-parametric approaches for GARCH models that avoid imposing arbitrary parametric distributional assumptions. These novel approaches implicitly assume infinite mixture of Gaussian distributions on the standardized returns which have been shown to be more flexible and describe better the uncertainty about future volatilities. Finally, the survey presents an illustration using real data to show the flexibility and usefulness of the non-parametric approach.
Subfields: Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/68a7c47b382e8778f7e20d3fc86ad9a09cd6fba0
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In Finance
- Xin Jin
- John M. Maheu
- Asger Lunde
- N. Shephard
- André Lucas
- Tim Bollerslev
- Shiu‐Sheng Chen
- Torben G. Andersen
In Statistics and Probability
Citations in the corpus
- Bayesian semiparametric modeling of realized covariance matrices (2016, background): “Some examples in finance include Delatola & Griffin (2013), Jensen & Maheu (2013a), Virbickaite et al. (2013) and Kalli & Griffin (2014).1 Therefore, a natural starting point for our work is a DPM model that mixes over distributions defined on positive definite matrices.”