2019/05/21 by Jessica Silva Lomba, Maria Isabel Fraga Alves
Computer Science · Economics, Econometrics and Finance · Mathematics · #Bayesian Methods and Mixture Models #Computational intelligence #Event (particle physics) #Extreme value theory #Financial Risk and Volatility Modeling #Generalized Pareto distribution #Inference #Pareto principle #Selection (genetic algorithm) #Sensitivity (control systems) #Statistical Distribution Estimation and Applications #Variance (accounting) #stat.AP #stat.ME
paper · pdf · doi:10.1007/s00477-020-01789-x
published as L-moments for automatic threshold selection in extreme value analysis. Stoch Environ Res Risk Assess 34, 465-491 (2020)
arxiv created 2019/05/21 · openalex created_date 2019/05/29 · openalex publication_date 2020/03/23 · arxiv updated 2020/09/01 · openalex updated_date 2026/08/05
In extreme value analysis, sensitivity of inference to the definition of extreme event is a paramount issue. Under the peaks-over-threshold (POT) approach, this translates directly into the need of fitting a Generalized Pareto distribution to observations above a suitable level that balances bias versus variance of estimates. Selection methodologies established in the literature face recurrent challenges such as an inherent subjectivity or high computational intensity. We suggest a truly automated method for threshold detection, aiming at time efficiency and elimination of subjective judgment. Based on the well-established theory of L-moments, this versatile data-driven technique can handle batch processing of large collections of extremes data, while also presenting good performance on small samples. The technique's performance is evaluated in a large simulation study and illustrated with significant wave height data sets from the literature. We find that it compares favorably to other state-of-the-art methods regarding the choice of threshold, associated parameter estimation and the ultimate goal of computationally efficient return level estimation.