Bayesian Inference Methods for Univariate and Multivariate GARCH Models: A Survey
Bayesian Inference Methods for Univariate and Multivariate GARCH Models: A Survey
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DOI：
10.1111/joes.12046
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发表时间：
2014-02
期刊：
影响因子：
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通讯作者：
Audronė Virbickaitė;M. C. Ausín;Pedro Galeano
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文献类型：
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作者：
Audronė Virbickaitė;M. C. Ausín;Pedro Galeano
本文综述了目前关于单变量和多变量GARCH模型最相关的贝叶斯推理方法的文献。概述了每种方法的优缺点，以及贝叶斯方法相对于经典方法的优点。本文重点介绍了GARCH模型的贝叶斯非参数方法，该方法避免强加任意参数分布假设。这些新颖的方法隐含地假设了标准化收益的高斯分布的无限混合，这已被证明是更灵活的，并更好地描述了未来波动的不确定性。最后，该调查提供了一个使用实际数据的说明，以显示非参数方法的灵活性和实用性。
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.