Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2019

Detecting outliers in multivariate volatility models: A wavelet procedure

  • Identification data

    Identifier: RP:4693
  • Authors:

    Veiga, Helena
    Martín-Barragán, Belén
    Grané, Aurea
  • Others:

    Author, as appears in the article.: Veiga, Helena Martín-Barragán, Belén Grané, Aurea
    Keywords: Correlations
    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.
    Journal publication year: 2019
    Publication Type: ##rt.metadata.pkp.peerReviewed## info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article