Peaks Over Thresholds Modeling With Multivariate Generalized Pareto Distributions
Journal article, 2019

Published with license by Taylor & Francis. When assessing the impact of extreme events, it is often not just a single component, but the combined behavior of several components which is important. Statistical modeling using multivariate generalized Pareto (GP) distributions constitutes the multivariate analogue of univariate peaks over thresholds modeling, which is widely used in finance and engineering. We develop general methods for construction of multivariate GP distributions and use them to create a variety of new statistical models. A censored likelihood procedure is proposed to make inference on these models, together with a threshold selection procedure, goodness-of-fit diagnostics, and a computationally tractable strategy for model selection. The models are fitted to returns of stock prices of four UK-based banks and to rainfall data in the context of landslide risk estimation. Supplementary materials and codes are available online.

Financial risk

Landslides

Multivariate extremes

Tail dependence

Author

Anna Kiriliouk

Erasmus School of Economics

Holger Rootzen

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Johan Segers

Universite catholique de Louvain

Jennifer L. Wadsworth

Lancaster University

Technometrics

0040-1706 (ISSN) 1537-2723 (eISSN)

Vol. 61 1 123-135

Subject Categories

Applied Mechanics

Other Civil Engineering

Probability Theory and Statistics

Roots

Basic sciences

DOI

10.1080/00401706.2018.1462738

More information

Latest update

5/25/2019