Please use this identifier to cite or link to this item:
|Title:||Detecting outliers in multivariate volatility models: a wavelet procedure|
Multivariate GARCH models
|Publisher:||INST ESTADISTICA CATALUNYA-IDESCAT|
|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.|
|Appears in Collections:||BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.