Modeling extremal dependence in multivariate and spatial problems: a practical perspective

TL;DR

This paper introduces practical methods for modeling multivariate and spatial extreme events using the R package ExtremalDep, with real-world applications to facilitate risk assessment in various fields.

Contribution

It provides accessible instructions and real-world examples for analyzing multivariate and spatial extremes with the ExtremalDep R package, bridging theory and practice.

Findings

Demonstrates the use of ExtremalDep in environmental and financial data analysis.

Shows how to assess risks of extreme events beyond observed data.

Provides a user-friendly guide for practitioners without advanced expertise.

Abstract

From environmental sciences to finance, there is a growing demand for methods that can assess the risks of extreme events beyond those observed in available data. Extrapolating extreme events beyond the range of the data is not obvious. Risk assessments are often further complicated by the need to account for multiple variables simultaneously. Extreme value theory provides important tools for the analysis of multivariate or spatial extreme events, but these are not easily accessible to professionals without appropriate expertise. This article provides a minimal background on multivariate and spatial extremes and gives simple yet thorough instructions on how to analyse them using the R package ExtremalDep. After briefly introducing the statistical methodologies, we focus on road testing the package's toolbox through several real-world applications.