We develop a non-parametric test of tail-specific extremal serial dependence for possibly heavy-tailed time series. The test statistic is asymptotically chi-squared under a null of "extremal white noise", as long as extremes of the time series are Near-Epoch-Dependent on the extremes of some mixing process. The theory covers ARFIMA, FIGARCH, bilinear, and Extremal Threshold processes, and a wide range of nonlinear distributed lags. In this setting the test statistic obtains an asymptotic power of one under the alternative. Of separate interest, we deliver a joint distribution limit for an arbitrary vector of tail index estimators under extraordinarily gene ral conditions, complete with a consistent kernel estimator of the covariance matrix. We apply tail specific tests to equity market and exchange rate returns data.
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Paper provided by Florida International University, Department of Economics in its series Working Papers with number
0513.
Find related papers by JEL classification: C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Econometric and Statistical Methods; Specific Distributions C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
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