Identification of asymmetric conditional heteroscedasticity in the presence of outliers

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