文章基本信息

标题：Detecting outliers in multivariate volatility models: A wavelet procedure
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作者：Aurea Grané ; Belén Martín-Barragán ; Helena Veiga 等
期刊名称：SORT-Statistics and Operations Research Transactions
印刷版ISSN：2013-8830
出版年度：2019
页码：289-316
DOI：10.2436/20.8080.02.89
出版社：SORT- Statistics and Operations Research Transactions
摘要：It is well known that outliers can affect both the estimation of parameters and volatilities when fitting a univariate GARCH-type model. Similar biases and impacts are expected to be found on correlation dynamics in the context of multivariate time series. We study the impact of outliers on the estimation of correlations when fitting multivariate GARCH models and propose a general detection algorithm based on wavelets, that can be applied to a large class of multivariate volatility models. Its effectiveness is evaluated through a Monte Carlo study before it is applied to real data. The method is both effective and reliable, since it detects very few false outliers.
关键词：Correlations;multivariate GARCH models;outliers;wavelets