Fast Clustering of GARCH Processes Via Gaussian Mixture Models
Fast Clustering of GARCH Processes Via Gaussian Mixture Models
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通过高斯混合模型对 GARCH 过程进行快速聚类
DOI：
10.2139/ssrn.2071716
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
M. Caporin
中科院分区：
文献类型：
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作者：
Gian Piero Aielli;M. Caporin
金融计量经济学文献包括几个多元GARCH模型，其中模型参数矩阵依赖于金融资产的聚类。这些类可以是先验定义的，也可以是数据驱动的。当采用后一种方法时，通过使用聚类方法给出了派生资产组的一种方法。本文详细分析了其中一种聚类方法——高斯混合GARCH。该方法基于条件方差动态参数进行群体识别。最近提出了基于高斯混合模型的聚类算法，并通过引入对资产之间存在相关性的校正进行了推广。最后，我们介绍了一个基准估计器，用于评估更简单和更快的估计器的性能。仿真实验证明了资产相关性校正所带来的改进。
The financial econometrics literature includes several multivariate GARCH models where the model parameter matrices depend on a clustering of financial assets. Those classes might be defined a priori or data-driven. When the latter approach is followed, one method for deriving asset groups is given by the use of clustering methods. In this paper, we analyze in detail one of those clustering approaches, the Gaussian Mixture GARCH. This method is designed to identify groups based on the conditional variance dynamic parameters. The clustering algorithm, based on a Gaussian Mixture model, has been recently proposed and is here generalized with the introduction of a correction for the presence of correlation across assets. Finally, we introduce a benchmark estimator used to assess the performances of simpler and faster estimators. Simulation experiments show evidence of the improvements given by the correction for asset correlation.