Stochastic volatility: Likelihood inference and comparison with ARCH models
Stochastic volatility: Likelihood inference and comparison with ARCH models
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DOI：
10.1111/1467-937x.00050
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发表时间：
1998-07-01
影响因子：
5.8
通讯作者：
Chib, S
中科院分区：
文献类型：
--
作者：
Kim, S;Shephard, N;Chib, S
在本文中，马尔可夫链蒙特卡罗抽样方法被利用，提供一个统一的，实用的基于似然的框架分析随机波动模型。一个非常有效的方法，样品所有未观测到的挥发性在一次使用一个近似的偏移混合模型，然后通过一个重要性重新加权程序。这种方法进行了比较与几种替代方法使用真实的数据。本文还开发了基于仿真的滤波，似然评估和模型故障诊断方法。使用非嵌套似然比和贝叶斯因子的模型选择的问题也进行了研究。这些方法被用来比较随机波动率和GARCH模型的拟合。所有的程序都有详细的说明。
In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating offset mixture model, followed by an importance reweighting procedure. This approach is compared with several alternative methods using real data. The paper also develops simulation-based methods for filtering, likelihood evaluation and model failure diagnostics. The issue of model choice using non-nested likelihood ratios and Bayes factors is also investigated. These methods are used to compare the fit of stochastic volatility and GARCH models. All the procedures are illustrated in detail.