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Identification of asymmetric conditional heteroscedasticity in the presence of outliers

Listed author(s):
  • Pérez, Ana
  • Carnero Fernández, María Ángeles
  • Ruiz Ortega, Esther

The identification of asymmetric conditional heteroscedasticity is often based on samplecross-correlations between past and squared observations. In this paper we analyse theeffects of outliers on these cross-correlations and, consequently, on the identification ofasymmetric volatilities. We show that, as expected, one isolated big outlier biases thesample cross-correlations towards zero and hence could hide true leverage effect.Unlike, the presence of two or more big consecutive outliers could lead to detectingspurious asymmetries or asymmetries of the wrong sign. We also address the problemof robust estimation of the cross-correlations by extending some popular robustestimators of pairwise correlations and autocorrelations. Their finite sample resistanceagainst outliers is compared through Monte Carlo experiments. Situations with isolatedand patchy outliers of different sizes are examined. It is shown that a modified Ramsayweightedestimator of the cross-correlations outperforms other estimators in identifyingasymmetric conditionally heteroscedastic models. Finally, the results are illustrated withan empirical application

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File URL: http://e-archivo.uc3m.es/bitstream/handle/10016/19095/ws141912.pdf?sequence=1
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Paper provided by Universidad Carlos III de Madrid. Departamento de Estadística in its series DES - Working Papers. Statistics and Econometrics. WS with number ws141912.

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Date of creation: Jul 2014
Handle: RePEc:cte:wsrepe:ws141912
Contact details of provider: Web page: http://portal.uc3m.es/portal/page/portal/dpto_estadistica

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  1. Marc Hallin & Madan Lal Puri, 1994. "Aligned rank tests for linear models with autocorrelated errors," ULB Institutional Repository 2013/2045, ULB -- Universite Libre de Bruxelles.
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  10. Céline Lévy‐Leduc & Hélène Boistard & Eric Moulines & Murad S. Taqqu & Valderio A. Reisen, 2011. "Robust estimation of the scale and of the autocovariance function of Gaussian short‐ and long‐range dependent processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(2), pages 135-156, 03.
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  16. Victor Gómez & Agustin Maravall & Daniel Peña, 1999. "Missing observations in ARIMA models: Skipping strategy versus outlier approach," Working Papers 9701, Banco de España;Working Papers Homepage.
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