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Extremal Correlation for GARCH Data


  • Carmela Quintos


This paper introduces a nonparametric estimator for tail dependence in the constant conditional correlation GARCH framework, in contrast to existing estimators that impose the iid assumption. So long as stationarity is satisfied, the difference between the distribution of the tail dependence estimator under the iid and GARCH case is a scaling variance. Without the scaling variance, tests based on this estimator overreject the null of asymptotic tail dependence. An empirical application to tail dependence between emerging market bonds and equities shows that there is tail depenedence in their joint density even though the standard linear correlation coefficients indicate low correlation between assets. These findings and the methods introduced here have implications for risk management and portfolio allocation theory that are based on the standard correlation estimato

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  • Carmela Quintos, 2004. "Extremal Correlation for GARCH Data," Econometric Society 2004 North American Summer Meetings 87, Econometric Society.
  • Handle: RePEc:ecm:nasm04:87

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    Cited by:

    1. Jonathan B. Hill, 2004. "Gaussian Tests of "Extremal White Noise" for Dependent, Heterogeneous, Heavy Tailed Time Series with an Application," Econometrics 0411014, EconWPA, revised 04 Nov 2005.

    More about this item


    GARCH; Extreme Value Theory;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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