A latent trawl process model for extreme values

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DOI

  • Ragnhild C. Noven, Imperial College London
  • ,
  • Almut E.D. Veraart
  • Axel Gandy, Imperial College London

This paper presents a new model for characterizing temporal dependence in exceedances above a given threshold. Our model is based on a class of stationary, infinitely divisible stochastic processes known as trawl processes. For use with extreme values, our model is constructed by embedding a trawl process in a hierarchical framework. This ensures that the marginal distribution is a generalized Pareto, as expected from classical extreme value theory. We also consider a modified version of this model that works with a wider class of generalized Pareto distributions (GPDs) and has the advantage of separating marginal and temporal dependence properties. The model is illustrated via various applications to environmental time series; thus, we show that the model offers considerable flexibility in capturing the dependence structure of extreme value data.

Original languageEnglish
JournalJournal of Energy Markets
Volume11
Issue3
Pages (from-to)1-24
Number of pages24
ISSN1756-3607
DOIs
Publication statusPublished - Sep 2018
Externally publishedYes

    Research areas

  • Conditional tail dependence coefficient, Generalized pareto distribution (GPD), Marginal transformation model, Pairwise likelihood estimation, Peaks over threshold, Trawl process

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