  To draw inference on serial extremal dependence within heavy-tailed Markov
chains, Drees, Segers and Warcho{\l} [Extremes (2015) 18, 369--402] proposed
nonparametric estimators of the spectral tail process. The methodology can be
extended to the more general setting of a stationary, regularly varying time
series. The large-sample distribution of the estimators is derived via
empirical process theory for cluster functionals. The finite-sample performance
of these estimators is evaluated via Monte Carlo simulations. Moreover, two
different bootstrap schemes are employed which yield confidence intervals for
the pre-asymptotic spectral tail process: the stationary bootstrap and the
multiplier block bootstrap. The estimators are applied to stock price data to
study the persistence of positive and negative shocks.
