A latent trawl process model for extreme values

• Main Authors: Gandy, A. (Author), Noven, R.C (Author), Veraart, A.E.D (Author)
• Format: Article
• Language: English
• Published: Infopro digital 2018
• Subjects: Conditional tail dependence coefficient; Generalized pareto distribution (GPD); Marginal transformation model; Pairwise likelihood estimation; Peaks over threshold; Trawl process
• Online Access: View Fulltext in Publisher

 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. © 2018 Infopro Digital Risk (IP) Limited.