标题： 贝叶斯推断在潜在因子GARCH模型中的应用 显示英文标题

标题： Bayesian inference for latent factor GARCH models

摘要： 潜在因子GARCH模型使用贝叶斯方法估计非常困难，因为标准的马尔可夫链蒙特卡罗抽样器产生的后验分布参数混合缓慢且效率低下。 本文描述了如何应用粒子吉布斯算法有效地估计因子GARCH模型。 该方法相比以前的方法有两个优势。 首先，它可以 straightforward 地推广到具有多个因子的模型以及GARCH族的各种成员。 其次，随着观测向量维度的增加，它能够很好地扩展。 显示英文摘要

摘要： 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.