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摘要翻译：
我们对非对称GARCH模型中的GJR-GARCH模型的贝叶斯推断进行了马尔可夫链蒙特卡罗模拟。Metropolis-Hastings算法采用自适应构造方案构造建议密度，利用Markov链Monte Carlo仿真采样数据自适应确定建议密度参数。我们用人工GJR-GARCH数据研究了该方案的性能。我们发现，自适应构造方案能够有效地对GJR-GARCH模型参数进行采样，并得出结论：采用自适应构造方案的Metropolis-Hastings算法是一种有效的GJR-GARCH模型贝叶斯推理方法。
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英文标题：
《Markov Chain Monte Carlo on Asymmetric GARCH Model Using the Adaptive
Construction Scheme》
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作者：
Tetsuya Takaishi
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最新提交年份：
2009
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分类信息：
一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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英文摘要：
We perform Markov chain Monte Carlo simulations for a Bayesian inference of the GJR-GARCH model which is one of asymmetric GARCH models. The adaptive construction scheme is used for the construction of the proposal density in the Metropolis-Hastings algorithm and the parameters of the proposal density are determined adaptively by using the data sampled by the Markov chain Monte Carlo simulation. We study the performance of the scheme with the artificial GJR-GARCH data. We find that the adaptive construction scheme samples GJR-GARCH parameters effectively and conclude that the Metropolis-Hastings algorithm with the adaptive construction scheme is an efficient method to the Bayesian inference of the GJR-GARCH model.
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PDF链接：
https://arxiv.org/pdf/0909.1478
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