RT info:eu-repo/semantics/article
T1 Identification of asymmetric conditional heteroscedasticity in the presence of outliers
A1 Carnero, María Ángeles
A1 Pérez Espartero, Ana
A1 Ruiz, Esther
K1 Estimación, teoría de la
AB The identification of asymmetric conditional heteroscedasticity is often based on sample cross-correlations between past and squared observations. In this paper we analyse  the effects of outliers on these cross-correlations and, consequently, on the identification of asymmetric volatilities. We showthat, as expected, one isolated big outlier biases the sample cross-correlations towards zero and hence could hide true leverage effect. Unlike, the presence of  two or more big consecutive outliers could lead to detecting spurious asymmetries or asymmetries of the wrong sign. We also address the problem of robust estimation of the cross-correlations by extending some popular robust estimators of pairwise correlations and autocorrelations. Their finite sample resistance against outliers is compared through Monte Carlo experiments. Situations with isolated and patchy outliers of different sizes are examined. It is shown that a modified Ramsay-weighted estimator of the cross-correlations outperforms other estimators in identifying asymmetric conditionally heteroscedastic models. Finally, the results are illustrated with an empirical application.
PB Springer Open
SN 1869-4187
YR 2016
FD 2016
LK http://uvadoc.uva.es/handle/10324/21496
UL http://uvadoc.uva.es/handle/10324/21496
LA eng
NO SERIEs: Journal of the Spanish Economic Association, 2016, vol. 7, Issue 1, p. 179–201
NO Producción Científica
DS UVaDOC
RD 19-sep-2024