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In this paper, we investigate temporal clusters of extremes defined assubsequent exceedances of high thresholds in a stationary time series. Twomeaningful features of these clusters are the probability distribution of thecluster size and the ordinal patterns within a cluster. Since these patternstake only the ordinal structure of consecutive data points into account themethod is robust under monotone transformations and measurement errors. Weverify the existence of the corresponding limit distributions in the frameworkof regularly varying time series, develop non-parametric estimators and showtheir asymptotic normality under appropriate mixing conditions. The performanceof the estimators is demonstrated in a simulated example and a real dataapplication to discharge data of the river Rhine.

Ordinal Patterns in Clusters of Subsequent Extremes of Regularly Varying Time Series