Spatial risk measure for gaussian processes

TL;DR

This paper investigates a spatial risk measure for Gaussian processes considering spatial dependence, using simulations and real data to analyze the excess damage function over a threshold.

Contribution

It extends the study of spatial risk measures to Gaussian processes, providing new insights into their behavior with real and simulated data.

Findings

Quantitative analysis of spatial risk for Gaussian processes.

Simulation results demonstrating the risk measure behavior.

Application to real data confirming the theoretical findings.

Abstract

In this paper, we study the quantitative behavior of a spatial risk measure corresponding to a damage function and a region, taking into account the spatial dependence of the underlying process. This kind of risk measure has already been introduced and studied for some max-stable processes in [Koch2015]. In this paper, we consider isotropic Gaussian processes and the excess damage function over a threshold. We performed a simulation study and a real data study.