Detecting outliers in multivariate volatility models : a wavelet procedure
Grané, Aurea (Universidad Carlos III de Madrid. Departamento de Estadística)
Martín-Barragán, Belén (University of Edinburgh Business Schoo)
Veiga, Helena (Instituto Universitario de Lisboa)

Date:	2019
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.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Correlations ; Multivariate GARCH models ; Outliers ; Wavelets
Published in:	SORT : statistics and operations research transactions, Vol. 43 Núm. 2 (July-December 2019) , p. 289-316, ISSN 2013-8830

Adreça alternativa: https://raco.cat/index.php/SORT/article/view/361423
DOI: 10.2436/20.8080.02.89

28 p, 2.6 MB

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