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To draw inference on serial extremal dependence within heavy-tailed Markovchains, Drees, Segers and Warcho{\l} [Extremes (2015) 18, 369--402] proposednonparametric estimators of the spectral tail process. The methodology can beextended to the more general setting of a stationary, regularly varying timeseries. The large-sample distribution of the estimators is derived viaempirical process theory for cluster functionals. The finite-sample performanceof these estimators is evaluated via Monte Carlo simulations. Moreover, twodifferent bootstrap schemes are employed which yield confidence intervals forthe pre-asymptotic spectral tail process: the stationary bootstrap and themultiplier block bootstrap. The estimators are applied to stock price data tostudy the persistence of positive and negative shocks.

Inference on the tail process with application to financial time series modelling