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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., Grané, A. & Veiga, H. (2010). Wavelet-based detection of outliers in financial time series. Computational Statistics and Data Analysis. 54 (11), 2580-2593
Exportar Referência (IEEE) 
A. Grané et al.,  "Wavelet-based detection of outliers in financial time series", in Computational Statistics and Data Analysis, vol. 54, no. 11, pp. 2580-2593, 2010
Exportar BibTeX 
@article{grané2010_1714820767587,
	author = "Grané, A. and Grané, A. and Veiga, H.",
	title = "Wavelet-based detection of outliers in financial time series",
	journal = "Computational Statistics and Data Analysis",
	year = "2010",
	volume = "54",
	number = "11",
	doi = "10.1016/j.csda.2009.12.010",
	pages = "2580-2593",
	url = "http://www.sciencedirect.com/science/article/pii/S0167947309004629?via%3Dihub"
}
Exportar RIS 
TY  - JOUR
TI  - Wavelet-based detection of outliers in financial time series
T2  - Computational Statistics and Data Analysis
VL  - 54
IS  - 11
AU  - Grané, A.
AU  - Grané, A.
AU  - Veiga, H.
PY  - 2010
SP  - 2580-2593
SN  - 0167-9473
DO  - 10.1016/j.csda.2009.12.010
UR  - http://www.sciencedirect.com/science/article/pii/S0167947309004629?via%3Dihub
AB  - Outliers in financial data can lead to model parameter estimation biases, invalid inferences and poor volatility forecasts. Therefore, their detection and correction should be taken seriously when modeling financial data. The present paper focuses on these issues and proposes a general detection and correction method based on wavelets that can be applied to a large class of volatility models. The effectiveness of the new proposal is tested by an intensive Monte Carlo study for six well-known volatility models and compared to alternative proposals in the literature, before it is applied to three daily stock market indices. The Monte Carlo experiments show that the new method is both very effective in detecting isolated outliers and outlier patches and much more reliable than other alternatives, since it detects a significantly smaller number of false outliers. Correcting the data of outliers reduces the skewness and the excess kurtosis of the return series distributions and allows for more accurate return prediction intervals compared to those obtained when the existence of outliers is ignored.
ER  -