Modelling Short- and Long-Term Dependencies of Clustered High-Threshold Exceedances in Significant Wave Heights

Pushpa Dissanayake, Teresa Flock, Johanna Meier and Philipp Sibbertsen
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Pushpa Dissanayake: Coastal Geology and Sedimentology, Institute of Geosciences, Kiel University, 24118 Kiel, Germany
Teresa Flock: Faculty of Economics and Management, Institute of Statistics, Leibniz University Hannover, 30167 Hannover, Germany
Johanna Meier: Faculty of Economics and Management, Institute of Statistics, Leibniz University Hannover, 30167 Hannover, Germany

Mathematics, 2021, vol. 9, issue 21, 1-33

Abstract: The peaks-over-threshold (POT) method has a long tradition in modelling extremes in environmental variables. However, it has originally been introduced under the assumption of independently and identically distributed (iid) data. Since environmental data often exhibits a time series structure, this assumption is likely to be violated due to short- and long-term dependencies in practical settings, leading to clustering of high-threshold exceedances. In this paper, we first review popular approaches that either focus on modelling short- or long-range dynamics explicitly. In particular, we consider conditional POT variants and the Mittag–Leffler distribution modelling waiting times between exceedances. Further, we propose a new two-step approach capturing both short- and long-range correlations simultaneously. We suggest the autoregressive fractionally integrated moving average peaks-over-threshold (ARFIMA-POT) approach, which in a first step fits an ARFIMA model to the original series and then in a second step utilises a classical POT model for the residuals. Applying these models to an oceanographic time series of significant wave heights measured on the Sefton coast (UK), we find that neither solely modelling short- nor long-range dependencies satisfactorily explains the clustering of extremes. The ARFIMA-POT approach, however, provides a significant improvement in terms of model fit, underlining the need for models that jointly incorporate short- and long-range dependence to address extremal clustering, and their theoretical justification.

Keywords: peaks-over-threshold; extremal clustering; long-range dependence; ARFIMA models; extreme value theory; significant wave heights; Sefton coast (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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Working Paper: Modelling Short- and Long-Term Dependencies of Clustered High-Threshold Exceedances in Significant Wave Heights (2021)
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