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

Detecting outliers in multivariate volatility models: A wavelet procedure

  • Datos identificativos

    Identificador: RP:4693
    Handle: https://hdl.handle.net/20.500.11797/RP4693
    Autores:
    Veiga, HelenaMartín-Barragán, BelénGrané, Aurea
    Resumen:
    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.

  • Otros:

    Autor según el artículo: Veiga, Helena Martín-Barragán, Belén Grané, Aurea
    Palabras clave: Correlations
    Resumen: 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.
    Año de publicación de la revista: 2019
    Tipo de publicación: ##rt.metadata.pkp.peerReviewed## info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article