Identification of asymmetric conditional heteroscedasticity in the presence of outliers Articles uri icon

publication date

  • March 2016

start page

  • 179

end page

  • 201

issue

  • 1

volume

  • 7

international standard serial number (ISSN)

  • 1869-4187

electronic international standard serial number (EISSN)

  • 1869-4195

abstract

  • 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 show that, 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.

keywords

  • cross-correlations; leverage effect; robust correlations; egarch; stochastic volatility model; garch models