Analysis of extreme values with random location

TL;DR

This paper explores modeling unobserved heterogeneity in the location parameter of extreme value distributions, enhancing the accuracy of extreme value analysis in fields like finance and climate science.

Contribution

It introduces a novel approach to account for unobserved heterogeneity in the location parameter within the block-maxima method of extreme value theory.

Findings

Improved modeling of extreme values with heterogeneity

Enhanced risk assessment accuracy

Potential applications in finance and climate forecasting

Abstract

Analysis of the rare and extreme values through statistical modeling is an important issue in economical crises, climate forecasting, and risk management of financial portfolios. Extreme value theory provides the probability models needed for statistical modeling of the extreme values. There are generally two ways to identifying the extreme values in a data set, the block-maxima and the peak-over threshold method. The block-maxima method uses the Generalized Extreme Value distribution and the peak-over threshold method uses the Generalized Pareto distribution. It is common that the location of these distributions kept fixed. It is possible that some unobserved variables produce heterogeneity in the location of the assumed distribution. In this article we focus on modeling this unobserved heterogeneity in block-maxima method.