Detecting outliers in multivariate volatility models: a wavelet procedure Articles uri icon

publication date

  • December 2019

start page

  • 289

end page

  • 316

issue

  • 2

volume

  • 43

International Standard Serial Number (ISSN)

  • 1696-2281

Electronic International Standard Serial Number (EISSN)

  • 2013-8830

abstract

  • 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.

subjects

  • Economics
  • Information Science
  • Mathematics
  • Statistics

keywords

  • correlations; multivariate garch models; outliers; wavelets