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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Grané, A., Martín-Barragan, B. & Veiga, H. (2019). Detecting outliers in multivariate volatility models: a wavelet procedure. Sort: Statistics and Operations Research Transactions. 43 (2), 289-315
Exportar Referência (IEEE) 
A. Grané et al.,  "Detecting outliers in multivariate volatility models: a wavelet procedure", in Sort: Statistics and Operations Research Transactions, vol. 43, no. 2, pp. 289-315, 2019
Exportar BibTeX 
@article{grané2019_1714722288448,
	author = "Grané, A. and Martín-Barragan, B. and Veiga, H.",
	title = "Detecting outliers in multivariate volatility models: a wavelet procedure",
	journal = "Sort: Statistics and Operations Research Transactions",
	year = "2019",
	volume = "43",
	number = "2",
	doi = "10.2436/20.8080.02.89",
	pages = "289-315",
	url = "https://www.raco.cat/index.php/SORT/article/view/361423"
}
Exportar RIS 
TY  - JOUR
TI  - Detecting outliers in multivariate volatility models: a wavelet procedure
T2  - Sort: Statistics and Operations Research Transactions
VL  - 43
IS  - 2
AU  - Grané, A.
AU  - Martín-Barragan, B.
AU  - Veiga, H.
PY  - 2019
SP  - 289-315
SN  - 1696-2281
DO  - 10.2436/20.8080.02.89
UR  - https://www.raco.cat/index.php/SORT/article/view/361423
AB  - 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.
ER  -